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Editor, IJSMI. "CART and CHAID ANALYSIS." International Journal of Statistics and Medical Informatics 15, no. 1 (2021): 1–6. https://doi.org/10.5281/zenodo.4672067.
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Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detector (CHAID) works on principles of decision tree analysis. Classification and Regression (CART) classifies the data based on the categorical outcome variable (Classification) and also uses continuous outcome variable for regression problem. Chi Square Automatic Interaction Detector (CHAID) is similar to CART which uses classifies the data into multiple class labels not only binary classification. In CHAID both dependent variable and independent variables will be categorical. This paper provides an overview and CART and CHAID methods using open source R software with hypothetical data set <strong>Keywords: </strong>Decision Tree, Classification, Regression, CART, CHAID,
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Pratiwi, Reni, Memi Nor Hayati, and Surya Prangga. "PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS : DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 2 (2020): 273–84. http://dx.doi.org/10.30598/barekengvol14iss2pp273-284.
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Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). C5.0 algorithm is a non-binary decision tree where the branch of tree can be more than two, while the CART algorithm is a binary decision tree where the branch of tree consists of only two branches. This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of family members (X2), last education (X3) and gender (X4). After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method.
 
 Keywords: C5.0 Algorithm, CART, Classification, Decision Tree.
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Bello, Safinatu, Ahmad Abubakar Aliyu, Muhammad Aminu Ahmad, et al. "AN ENHANCED CLASSIFICATION AND REGRESSION TREE ALGORITHM USING GINI EXPONENTIAL." FUDMA JOURNAL OF SCIENCES 9, no. 3 (2025): 259–67. https://doi.org/10.33003/fjs-2025-0903-3321.
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Decision tree algorithms, particularly Classification and Regression Trees (CART), are widely used in machine learning for their simplicity, interpretability, and ability to handle both categorical and numerical data. However, traditional decision trees often encounter limitations when dealing with complex, high-dimensional, or imbalanced datasets, as conventional impurity measures such as the Gini Index and Information Gain may fail to capture subtle variations in the data effectively. This study enhances the traditional Classification and Regression Trees (CART) model by introducing the Gini Exponential Criterion, which incorporates an exponential weighting factor into the split point calculation process. This novel approach amplifies the influence of highly discriminative features, resulting in more refined splits and improved decision boundaries. The enhanced CART model was evaluated on two benchmark datasets: the wine quality dataset and the hypothyroid dataset, with preprocessing steps like feature scaling and SMOTE for class imbalance, and hyperparameter tuning via Bayesian Optimization. On the wine quality dataset, the enhanced model improved accuracy from 57% (traditional CART) to 86%, while on the hypothyroid dataset, it achieved an impressive accuracy of 98%. These results highlight the model's ability to handle complex and imbalanced data effectively. Feature importance analysis and decision tree visualization further demonstrated the model's interpretability. The study concludes that the Gini Exponential Criterion significantly improves CART's performance, offering better generalization and clearer decision boundaries. This advancement is particularly valuable for applications requiring precise and interpretable predictions, such as healthcare diagnostics and quality assessment. Future work could explore integrating this criterion into ensemble methods and...
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Suwardika, Gede, I. Ketut Putu Suniantara, and Ni Putu Nanik Hendayanti. "Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart." Jurnal VARIAN 2, no. 2 (2019): 37–46. http://dx.doi.org/10.30812/varian.v2i2.361.
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The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one method that combines many classification trees to improve stability and determine classification predictions. This research purpose to improve the stability and predictive accuracy of CART with boosting. The case used in this study is the classification of inaccuracies in the Open University student graduation. The results of the analysis show that boosting is able to improve the accuracy of the classification of the inaccuracy of student graduation which reaches a classification prediction of 75.94% which previously reached 65.41% in the classification tree.
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Suwardika, Gede Suwardika, and I. Ketut Putu Suniantara. "ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 13, no. 3 (2019): 177–84. http://dx.doi.org/10.30598/barekengvol13iss3pp177-184ar910.
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Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.
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Fan, Zhaofei, John M. Kabrick, and Stephen R. Shifley. "Classification and regression tree based survival analysis in oak-dominated forests of Missouri's Ozark highlands." Canadian Journal of Forest Research 36, no. 7 (2006): 1740–48. http://dx.doi.org/10.1139/x06-068.
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Tree survival or mortality is a stochastic process and highly variable over time and space. Many factors contribute to this process, including tree age, tree size, competition, drought, insects, and diseases. Traditional parametric approaches to modeling tree survival or mortality are often unable to capture this variation, especially in natural, mixed-species forests. We analyzed tree survival in Missouri Ozark oak forests using a combination of classification and regression tree (CART) and survival analysis of more than 35 000 trees with DBH >11 cm measured four times between 1992 and 2002. We employed a log-rank test with CART to classify trees into seven disjoint survival groups and used a nonparametric KaplanMeier (product limit) method to estimate tree survival rate and construct confidence intervals for each survival group. We found that tree species, crown class, DBH, and basal area of larger trees were the variables most closely associated with differences in tree survival rates. In these mature oak forests, mortality for the red oak species group was three to six times greater than for the white oak, hickory, or shortleaf pine species group. The results provide practical information to guide development of silvicultural prescriptions to reduce losses to mortality.
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Firman, Aziz, and Lawi Armin. "Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2955–62. https://doi.org/10.11591/ijece.v12i3.pp2955-2962.
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The increasing demand for electricity every year makes the electricity infrastructure approach the maximum threshold value, thus affecting the stability of the electricity network. The decentralized smart grid control (DSGC) system has succeeded in maintaining the stability of the electricity network with various assumptions. The data mining approach on the DSGC system shows that the decision tree algorithm provides new knowledge, however, its performance is not yet optimal. This paper poses an ensemble bagging algorithm to reinforce the performance of decision trees C4.5 and classification and regression trees (CART). To evaluate the classification performance, 10-fold cross-validation was used on the grid data. The results showed that the ensemble bagging algorithm succeeded in increasing the performance of both methods in terms of accuracy by 5.6% for C4.5 and 5.3% for CART.
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Aguspri, Amellia. "Analisis Financial Distress pada Bank Syariah di Indonesia Menggunakan Metode Classification and Regression Tree (CART)." Jurnal Ilmiah Ekonomi Islam 10, no. 1 (2024): 292. http://dx.doi.org/10.29040/jiei.v10i1.11148.
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Using the Grover and Classification and Regression Tree (CART) techniques, this research aims to evaluate the factors that influence the financial conditions of Islamic banks from 2010 to 2021. The sample for this research was taken using a purposive sampling technique from five banks registered in OJK from 2010 to 2021. The results of this research using Grover analysis show that no bank has experienced bankruptcy with an accuracy value of 100%. On the other hand, according to decision tree regression using the Classification and Regression Trees (CART) method, FDR, NPM and CAR are the three factors that influence financial bankruptcy. And it is known that of the three ratios, FDR is the most significant.
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Stewart, J. Richard. "Applications of Classification and Regression Tree Methods in Roadway Safety Studies." Transportation Research Record: Journal of the Transportation Research Board 1542, no. 1 (1996): 1–5. http://dx.doi.org/10.1177/0361198196154200101.
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In recent years a number of nonparametric regression-type statistical procedures have been developed. Classification and regression trees (CART) is one such method that can be used as a classifier for a discrete-valued response variable or as a regression model for a continuous response variable. Advantages of CART over many other methods are its ability to include a relatively large number of independent variables and to identify complex interactions among these variables. A brief description of the CART procedure is given, and its application as a classifier and as a regression model to highway safety analyses is illustrated.
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Fearn, Tom. "Classification and Regression Trees (CART)." NIR news 17, no. 6 (2006): 13–14. http://dx.doi.org/10.1255/nirn.917.
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Yeh, Chyon-Hwa. "Classification and regression trees (CART)." Chemometrics and Intelligent Laboratory Systems 12, no. 1 (1991): 95–96. http://dx.doi.org/10.1016/0169-7439(91)80113-5.
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Garzotto, Mark, Tomasz M. Beer, R. Guy Hudson, et al. "Improved Detection of Prostate Cancer Using Classification and Regression Tree Analysis." Journal of Clinical Oncology 23, no. 19 (2005): 4322–29. http://dx.doi.org/10.1200/jco.2005.11.136.
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Purpose To build a decision tree for patients suspected of having prostate cancer using classification and regression tree (CART) analysis. Patients and Methods Data were uniformly collected on 1,433 referred men with a serum prostate-specific antigen (PSA) levels of ≤ 10 ng/mL who underwent a prostate biopsy. Factors analyzed included demographic, laboratory, and ultrasound data (ie, hypoechoic lesions and PSA density [PSAD]). Twenty percent of the data was randomly selected and reserved for study validation. CART analysis was performed in two steps, initially using PSA and digital rectal examination (DRE) alone and subsequently using the remaining variables. Results CART analysis selected a PSA cutoff of more than 1.55 ng/mL for further work-up, regardless of DRE findings. CART then selected the following subgroups at risk for a positive biopsy: (1) PSAD more than 0.165 ng/mL/cc; (2) PSAD ≤ 0.165 ng/mL/cc and a hypoechoic lesion; (3) PSAD ≤ 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and prostate volume ≤ 44.0 cc; and (4) PSAD ≤ 0.165 ng/mL/cc, no hypoechoic lesions, age older than 55.5 years, and 50.25 cc less than prostate volume ≤ 80.8 cc. In the validation data set, specificity and sensitivity were 31.3% and 96.6%, respectively. Cancers that were missed by the CART were Gleason score 6 or less in 93.4% of cases. Receiver operator characteristic curve analysis showed that CART and logistic regression models had similar accuracy (area under the curve = 0.74 v 0.72, respectively). Conclusion Application of CART analysis to the prostate biopsy decision results in a significant reduction in unnecessary biopsies while retaining a high degree of sensitivity when compared with the standard of performing a biopsy of all patients with an abnormal PSA or DRE.
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Sharma, Dr Nirmla, and Sameera Iqbal Muhmmad Iqbal. "Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits." International Journal of Engineering and Advanced Technology 12, no. 5 (2023): 77–81. http://dx.doi.org/10.35940/ijeat.e4195.0612523.
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Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e., the objective parameter into diverse subsets which are comparatively more similar from the viewpoint of the objective parameter. Gini index is the name of the level task that has applied to assess the binary changes in the dataset and worked with the definite object variable “Success” or “Failure”. Split creation is basically covering the dataset values. Decision trees monitor a top-down, greedy method that has recognized as recursive binary splitting. It has statistics for 15 statistics facts of scholar statistics on pass or fails an online Machine Learning exam. Decision trees are in the class of supervised machine learning. It has been commonly applied as it has informal implement, interpreted certainly, derived to quantitative, qualitative, nonstop, and binary splits, and provided consistent outcomes. The CART tree has regression technique applied to expected standards of nonstop variables. CART regression trees are an actual informal technique of understanding outcomes.
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Dr., Nirmla Sharma, and Iqbal Muhmmad Iqbal Sameera. "Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 5 (2023): 77–81. https://doi.org/10.35940/ijeat.E4195.0612523.
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<strong>Abstract: </strong>Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e., the objective parameter into diverse subsets which are comparatively more similar from the viewpoint of the objective parameter. Gini index is the name of the level task that has applied to assess the binary changes in the dataset and worked with the definite object variable “Success” or “Failure”. Split creation is basically covering the dataset values. Decision trees monitor a top-down, greedy method that has recognized as recursive binary splitting. It has statistics for 15 statistics facts of scholar statistics on pass or fails an online Machine Learning exam. Decision trees are in the class of supervised machine learning. It has been commonly applied as it has informal implement, interpreted certainly, derived to quantitative, qualitative, nonstop, and binary splits, and provided consistent outcomes. The CART tree has regression technique applied to expected standards of nonstop variables. CART regression trees are an actual informal technique of understanding outcomes.
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Egelberg, Jacob, Nina Pena, Rachel Rivera, and Christina Andruk. "Assessing the geographic specificity of pH prediction by classification and regression trees." PLOS ONE 16, no. 8 (2021): e0255119. http://dx.doi.org/10.1371/journal.pone.0255119.
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Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The current study collected soil pH, climatic, and topographic data from 100 locations across New York’s Temperate Deciduous Forests (in the United States of America) to investigate the extrapolative capacity of a previously developed CART model as compared to novel CART and random forest (RF) models. Results showed that the previously developed CART underperformed in terms of predictive accuracy (RRMSE = 14.52%) when compared to a novel tree (RRMSE = 9.33%), and that a novel random forest outperformed both models (RRMSE = 8.88%), though its predictions did not differ significantly from the novel tree (p = 0.26). The most important predictors for model construction were climatic factors. These findings confirm existing reports that CART models are constrained by the spatial autocorrelation of geographic data and encourage the restricted application of relevant machine learning models to regions from which training data was collected. They also contradict previous literature implying that random forests should meaningfully boost the predictive accuracy of CARTs in the context of soil pH.
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Pratiwi, Reni, Memi Nor Hayati, and Surya Prangga. "PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 14, no. 2 (2020): 267–78. https://doi.org/10.30598/barekengvol14iss2pp267-278.
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Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of family members (X2), last education (X3) and gender (X4). After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method
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Aziz, Firman, and Armin Lawi. "Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2955. http://dx.doi.org/10.11591/ijece.v12i3.pp2955-2962.
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<span>The increasing demand for electricity every year makes the electricity infrastructure approach the maximum threshold value, thus affecting the stability of the electricity network. The decentralized smart grid control (DSGC) system has succeeded in maintaining the stability of the electricity network with various assumptions. The data mining approach on the DSGC system shows that the decision tree algorithm provides new knowledge, however, its performance is not yet optimal. This paper poses an ensemble bagging algorithm to reinforce the performance of decision trees C4.5 and classification and regression trees (CART). To evaluate the classification performance, 10-fold cross-validation was used on the grid data. The results showed that the ensemble bagging algorithm succeeded in increasing the performance of both methods in terms of accuracy by 5.6% for C4.5 and 5.3% for CART.</span>
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Xie, Xun, Gen Li, Lan Wu, and Shuxin Du. "Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data." Sensors 24, no. 22 (2024): 7225. http://dx.doi.org/10.3390/s24227225.
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Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data.
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Maesya, A., and T. Hendiyanti. "Forecasting Student Graduation With Classification And Regression Tree (CART) Algorithm." IOP Conference Series: Materials Science and Engineering 621 (November 7, 2019): 012005. http://dx.doi.org/10.1088/1757-899x/621/1/012005.
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Iorio, Carmela, Massimo Aria, Antonio D'Ambrosio, and Roberta Siciliano. "Informative trees by visual pruning." Expert Systems with Applications 127 (August 1, 2019): 228–40. https://doi.org/10.1016/j.eswa.2019.03.018.
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The aim of this study is to provide visual pruning and decision tree selection for classification and regression trees. Specifically, we introduce an unedited tree graph to be made informative for recursive tree data partitioning. A decision tree is visually selected through a dendrogram-like procedure or through automatic tree-size selection. Our proposal is a one-step procedure whereby the most predictive paths are visualized. This method appears to be useful in all real world cases where tree-path interpretation is crucial. Experimental evaluations using real world data sets are presented. The performance was very similar to Classification and Regression Trees (CART) benchmarking methodology, showing that our method is a valid alternative to the well-known method of cost-complexity pruning.
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Cho, Hyunsun, and Eun-Kyung Lee. "Tree-Structured Regression Model Using a Projection Pursuit Approach." Applied Sciences 11, no. 21 (2021): 9885. http://dx.doi.org/10.3390/app11219885.
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In this paper, a new tree-structured regression model—the projection pursuit regression tree—is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability.
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Djuniar, Seruni Purwanti, and Anneke Iswani Achmad. "Metode Classification And Regression Trees untuk Pengklasifikasian Faktor-Faktor yang Mempengaruhi Pengangguran Terbuka di Provinsi Jawa Barat Tahun 2020." Bandung Conference Series: Statistics 2, no. 2 (2022): 35–43. http://dx.doi.org/10.29313/bcss.v2i2.3038.
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Abstract. CART (Classification And Regression Trees) is a classification method that uses historical data to build decision trees. The CART method is used to form a classification tree using the gini index value obtained from the probability value of each node candidate. The CART method used aims to look at the factors that influence open unemployment in West Java Province in 2020. In this study, the independent variables that affect open unemployment are gender (X1), age (X2), education (X3), and marital status (X4), while for the status variable in the household (X5) from the classification tree results indicate that this variable is not a differentiating variable. From the results of the classification tree research formed, the results obtained for respondents with low and high levels of education who are male, both married and unmarried are included in the working classification, while for respondents with low and high education levels, If a woman is not married, then she works, while if she is married, she is considered unemployed. And for respondents with secondary education level, both male and female, who are married or not included in the unemployment classification. In this study, the classification of open unemployment resulted in an accuracy of classification on the testing data of 69.79%.
 Abstrak. CART (Classification And Regression Trees) adalah metode klasifikasi yang menggunakan data historis untuk membangun pohon keputusan. Metode CART digunakan untuk membentuk pohon klasifikasi dengan menggunakan nilai indeks gini yang didapat dari nilai probabilitas setiap calon simpul. Metode CART yang digunakan bertujuan untuk melihat faktor-faktor yang mempengaruhi pengangguran terbuka di Provinsi Jawa Barat tahun 2020. Dalam penelitian ini, variabel bebas yang berpengaruh terhadap pengangguran terbuka yaitu jenis kelamin (X1), usia (X2), pendidikan (X3), dan status pernikahan (X4), sementara untuk variabel status dalam rumah tangga (X5) dari hasil pohon klasifikasi menunjukkan bahwa variabel tersebut bukanlah sebagai variabel pembeda. Dari hasil penelitian pohon klasifikasi yang terbentuk, maka didapatkan hasil untuk responden dengan tingkat pendidikan rendah serta tinggi yang berjenis kelamin laki-laki, baik yang sudah menikah maupun yang belum menikah termasuk ke dalam klasifikasi bekerja, sedangkan untuk responden dengan tingkat pendidikan rendah dan tinggi, yang berjenis kelamin perempuan jika belum menikah maka ia bekerja, sedangkan yang sudah menikah maka ia termasuk pengangguran. Dan untuk responden dengan tingkat pendidikan menengah dengan jenis kelamin laki-laki maupun perempuan, yang sudah menikah maupun belum termasuk ke dalam klasifikasi pengangguran. Dalam penelitian ini pula, pengklasifikasian pengangguran terbuka menghasilkan ketepatan klasifikasi pada data testing sebesar 69,79%.
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Nur Fadillah, Nur, Syafriandi Syafriandi, Nonong Amalita, and Dony Permana. "Classification of Unemployment at West Sumatra Province in 2021 using Algorithm Classification and Regression Tree." UNP Journal of Statistics and Data Science 2, no. 2 (2024): 179–86. http://dx.doi.org/10.24036/ujsds/vol2-iss2/166.
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The problem of unemployment is a problem that often occurs in developing countries. This is caused by an imbalance between the number of the workforce and the number of working people. According to the Central Statistics Agency, West Sumatra Province in 2021 is the eighth province that has a high open unemployment rate namely 6,52%, which is higher than the average open unemployment rate in Indonesia namely 6,49%. An increase in unemployment has occurred from 2017 to 2021 which is caused by educated unemployment. This is caused by the habits of job seekers who tend to choose existing types of work, while business needs are very limited. The unemployment problem will get higher if it is not addressed. As a result, unemployment can cause poverty and other social problems. In this study, CART analysis was used to classify unemployment in West Sumatra Province in 2021, which aims to determine the factors that influence unemployment. Classification and Regression Tree (CART) is a decision tree that describes the relationship between a response variable and one or more predictor variables. The purpose of CART analysis is to obtain accurate data groups as characteristics of a classification. Based on the analysis obtained, the variables that infuence unemployment in West Sumatra Province in 2021 are the variables of marital status, gender, household status, education level, age, and place of residence with an accuracy value of 71,73%. Keywords: CART, Unemployment, Classification
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Erliani, Neli, Kris Suryowati, and Maria Titah Jatipaningrum. "KLASIFIKASI TINGKAT PENJUALAN LAPTOP DI E-COMMERCE MENGGUNAKAN ALGORITMA CLASSIFICATION AND REGRESSION TREE (CART)." Jurnal Statistika Industri dan Komputasi 8, no. 2 (2023): 40–47. http://dx.doi.org/10.34151/statistika.v8i2.4455.
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Seiring perkembangan zaman laptop sudah menjadi kebutuhan dasar bagi masyarakat dalam kegiatan sehari-hari, seperti melakukan kegiatan belajar, mengajar, bekerja, bahkan berbelanja, hal inilah yang menyebabkan meningkatnya pembelian laptop melalui E-commerce. Penelitian ini bertujuan untuk mengetahui karakteristik tingkat penjualan laptop di E-commerce sehingga dapat memberikan gambaran kepada calon pembeli dalam menentukan laptop yang akan dibeli sesuai dengan budget yang disediakan dan kriteria yang diinginkan serta dapat memberikan gambaran kepada pemilik toko dalam meningkatkan penjualan laptop di E-commerce. Pada penelitian ini metode klasifikasi yang digunakan adalah classification and regression tree (CART). CART merupakan salah satu algoritma decision tree yang dapat digunakan untuk melakukan klasifikasi menggunakan struktur hirarki atau pohon. Algoritma CART memiliki kelebihan yang sesuai dengan data penelitian yaitu CART dapat digunakan untuk klasifikasi dengan jumlah data yang cukup besar dengan banyak faktor serta dapat melakukan analisis klasifikasi pada peubah respon baik nominal, ordinal, maupun kontinu. Hasil dari penelitian ini menunjukkan bahwa model pohon keputusan yang terbentuk dari algoritma CART menghasilkan kedalaman lima, variabel yang berpengaruh terhadap tingkat penjualan laptop di E-commerce yaitu jumlah ulasan, harga dan jumlah produk dilihat, variabel jumlah ulasan menjadi akar atau merupakan variabel yang paling penting terhadap tingkat penjualan laptop di E-commerce. Algoritma classification and regression tree (CART) menghasilkan nilai akurasi yang sangat tinggi yaitu sebesar 95,10% sehingga algoritma CART dapat digunakan untuk melakukan klasifikasi tingkat penjualan laptop di E-commerce.
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Taghipour Zahir, Shokouh, Fariba Binesh, Mehrdad Mirouliaei, Elias Khajeh, and Sina Noshad. "Malignancy Risk Assessment in Patients with Thyroid Nodules Using Classification and Regression Trees." Journal of Thyroid Research 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/983953.
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Purpose.We sought to investigate the utility of classification and regression trees (CART) classifier to differentiate benign from malignant nodules in patients referred for thyroid surgery.Methods.Clinical and demographic data of 271 patients referred to the Sadoughi Hospital during 2006–2011 were collected. In a two-step approach, a CART classifier was employed to differentiate patients with a high versus low risk of thyroid malignancy. The first step served as the screening procedure and was tailored to produce as few false negatives as possible. The second step identified those with the lowest risk of malignancy, chosen from a high risk population. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the optimal tree were calculated.Results.In the first step, age, sex, and nodule size contributed to the optimal tree. Ultrasonographic features were employed in the second step with hypoechogenicity and/or microcalcifications yielding the highest discriminatory ability. The combined tree produced a sensitivity and specificity of 80.0% (95% CI: 29.9–98.9) and 94.1% (95% CI: 78.9–99.0), respectively. NPV and PPV were 66.7% (41.1–85.6) and 97.0% (82.5–99.8), respectively.Conclusion.CART classifier reliably identifies patients with a low risk of malignancy who can avoid unnecessary surgery.
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Xu, Xuecai, Željko Šarić, and Ahmad Kouhpanejade. "Freeway Incident Frequency Analysis Based on CART Method." PROMET - Traffic&Transportation 26, no. 3 (2014): 191–99. http://dx.doi.org/10.7307/ptt.v26i3.1308.
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Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.
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Irmanita, Rachmadania, Sri Suryani Prasetiyowati, and Yuliant Sibaroni. "Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (2021): 10–16. http://dx.doi.org/10.29207/resti.v5i1.2770.
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Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.
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Arifuzzaman, Md, Uneb Gazder, Md Shah Alam, Okan Sirin, and Abdullah Al Mamun. "Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis." Computational Intelligence and Neuroscience 2019 (August 15, 2019): 1–7. http://dx.doi.org/10.1155/2019/3183050.
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The modification by polymers and nanomaterials can significantly improve different properties of asphalt. However, during the service life, the oxidation affects the constituents of modified asphalt and subsequently results in deviation from the desired properties. One of the important properties affected due to oxidation is the adhesive properties of modified asphalt. In this study, the adhesive properties of asphalt modified with the polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated. Asphalt samples were aged in the laboratory by simulating the field conditions, and then adhesive properties were evaluated by different tips of atomic force microscopy (AFM) following the existing functional group in asphalt. Finally, a predictive modelling and machine learning technique called the classification and regression tree (CART) was used to predict the adhesive properties of modified asphalt subjected to oxidation. The parameters that affect the behaviour of asphalt have been used to predict the results using the CART. The results obtained from CART analysis were also compared with those from the regression model. It was observed that the CART analysis shows more explanatory relationships between different variables. The model can predict accurately the adhesive properties of modified asphalts considering the real field oxidation and chemistry of asphalt at a nanoscale.
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Dobbertin, Matthias, and Gregory S. Biging. "Using the Non-Parametric Classifier CART to Model Forest Tree Mortality." Forest Science 44, no. 4 (1998): 507–16. http://dx.doi.org/10.1093/forestscience/44.4.507.
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Abstract A binary classification tree (CART) was used to predict forest tree mortality for two conifer species. CART models were fitted using binary recursive splitting of the data set into increasingly homogeneous subsets. Models were compared in terms of improvement of prediction accuracy, representativeness in average size of the predicted mortality trees, and interpretability of the results. For shade intolerant ponderosa pine, crown ratio, diameter increment prediction, or variables indicating the relative position of a tree in a stand were used for splitting. For shade tolerant white fir, height increment prediction and stand density were selected for splitting. The prediction accuracies for mortality trees of the best CART models were between 28-36% for ponderosa pine and between 11-17% for white fir. CART was also compared with logistic regression using a stochastic and a deterministic assignment of mortality. Efficiencies similar to those achieved with CART were reached with deterministic logistic models using thresholds probabilities. However, CART and the logistic model tended to utilize different predictor variables, especially for white fir. CART uncovered additional factors for white fir important for predicting mortality not identified by the logistic regression. For. Sci. 44(4):507-516.
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Peute, Linda, Thom Scheeve, and Monique Jaspers. "Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study." Journal of Medical Internet Research 22, no. 1 (2020): e12509. http://dx.doi.org/10.2196/12509.
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Background There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. Objective The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. Methods NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. Results With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. Conclusions Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.
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Pangestuti, Ayu, Rachmah Indawati, and Diah Indriani. "PENERAPAN METODE CLASSIFICATION AND REGRESSION TREE (CART) DALAM KLASIFIKASI STROKE DI RS X." Jurnal Kesehatan Tambusai 6, no. 1 (2025): 2639–47. https://doi.org/10.31004/jkt.v6i1.41257.
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Metode klasifikasi, baik parametrik maupun non-parametrik, sering digunakan dalam penelitian untuk mengelompokkan data secara sistematis. Namun metode parametrik memiliki keterbatasan seperti pemenuhan asumsi dan penyederhanaan interpretasi. Metode non-parametrik, meskipun lebih mudah diinterpretasikan. Prevalensi stroke di Indonesia meningkat, dengan data rekam medis pasien stroke menunjukkan pentingnya penerapan metode klasifikasi dalam memahami dan mengelola risiko stroke untuk penanganan yang lebih cepat dan tepat. Penelitian ini menggunakan data rekam medis pasien stroke yang dirawat inap di RS X dan mengaplikasikan metode CART untuk klasifikasi faktor risiko dengan jumlah 480 kasus stroke pertama kali dengan 14 variabel prediktor. Metode yang digunakan dalam analisis menggunakan Classification and Regression Tree (CART). Tahapan analisis CART meliputi pembentukan pohon klasifikasi maksimal, pemilahan variabel prediktor, perhitungan keberagaman data, pemilahan simpul berdasarkan indeks gini, dan pemangkasan pohon untuk mendapatkan pohon optimal. Setelah dilakukan analisis, maka dapat dihitung akurasi, sensitivity, specivity, APER, dan presisi dalam metode yang dihasilkan CART. Berdasarkan hasil analisis, dapat diketahui bahwa ada 8 variabel yang penting, yaitu GCS UGD, gejala klinis muntah, tekanan darah sistolik rawat inap, riwayat diabetes mellitus, tekanan darah diastolik rawat inap, GDA dan kolesterol total. Hasil prediksi untuk metode CART pada akurasi sebesar 81%, sensitivity 82%, specivity 75%, APER 19%, dan presisi 95%. Simpulan dari penelitian ini menunjukkan metode CART efektif dalam klasifikasi faktor risiko stroke dengan akurasi 81%. Delapan variabel utama telah diidentifikasi, menunjukkan pentingnya penerapan metode klasifikasi untuk memahami dan mengelola risiko stroke guna meningkatkan efektivitas penanganan pasien.
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Yang, Bao Hua, and Shuang Li. "Remote Sense Image Classification Based on CART Algorithm." Advanced Materials Research 864-867 (December 2013): 2782–86. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.2782.
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This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.
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Fariskha Aninda Nurdifa. "Penggunaan Metode Classification And Regression Tree (CART) Dalam Mengklasifikasikan Faktor Yang Mempengaruhi Penyakit Diabetes." Jurnal Ilmiah Matematika 10, no. 2 (2025): 83–92. https://doi.org/10.26555/jim.v10i2.30878.
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Diabetes adalah penyakit kronis yang ditandai oleh tingginya kadar gula (glukosa) dalam darah. Kondisi ini terjadi ketika tubuh tidak mampu memproduksi atau menggunakan insulin dengan baik. Ada banyak faktor yang bisa mempengaruhi seseorang terkena penyakit diabetes, karenanya diperlukan klasifikasi faktor apa saja yang paling sering menyebabkan penyakit diabetes. Dalam skripsi ini penulis melakukan klasifikasi menggunakan metode Classification and Regression Tree (CART). Data yang digunakan dalam penelitian ini yaitu data penderita diabetes yang bersumber dari kaggle. Hasil penelitian menunjukkan bahwa diperoleh tingkat keakurasian algoritma Decision Tree Classification and Regression Tree (CART) dengan menggunakan confusion matrix menunjukkan bahwa tingkat sensitivity atau ketepatan prediksi pada kelas diabetes sebesar 100%, sedangkan tingkat specificity atau tingkat ketepatan prediksi pada kelas tidak diabetes sebesar 94.4%. Kemudian tingkat akurasi yang diperoleh mencapai 96.6%. Berdasarkan ketiga hasil tersebut, maka metode CART dapat digunakan untuk mengklasifikasikan penyakit diabetes secara optimal dengan hasil yang cukup baik.
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Okada, Hugo Kenji Rodrigues, Andre Ricardo Nascimento das Neves, and Ricardo Shitsuka. "Analysis of Decision Tree Induction Algorithms." Research, Society and Development 8, no. 11 (2019): e298111473. http://dx.doi.org/10.33448/rsd-v8i11.1473.
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Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.
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Uddameri, Venkatesh, Ana Silva, Sreeram Singaraju, Ghazal Mohammadi, and E. Hernandez. "Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas." Water 12, no. 4 (2020): 1023. http://dx.doi.org/10.3390/w12041023.
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The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnerability in the Ogallala Aquifer of Texas. The results indicate that the tree-based models performed better than the logistic regression model, as they were able to locally refine nitrate exceedance probabilities. RF exhibited the best generalizable capabilities. The CART model did better in predicting non-exceedances. Nitrate exceedances were sensitive to well depths—an indicator of aquifer redox conditions, which, in turn, was controlled by alkalinity increases brought forth by the dissolution of calcium carbonate. The clay content of soils and soil organic matter, which serve as indicators of agriculture activities, were also noted to have significant influences on nitrate exceedances. Likely nitrogen releases from confined animal feedlot operations in the northeast portions of the study area also appeared to be locally important. Integrated soil, hydrogeological and geochemical datasets, in conjunction with tree-based methods, help elucidate processes controlling nitrate exceedances. Overall, tree-based models offer flexible, transparent approaches for mapping nitrate exceedances, identifying underlying mechanisms and prioritizing monitoring activities.
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PERSONA, ALESSANDRO, DARIA BATTINI, MAURIZIO FACCIO, MAURIZIO BEVILACQUA, and FILIPPO EMANUELE CIARAPICA. "CLASSIFICATION OF OCCUPATIONAL INJURY CASES USING THE REGRESSION TREE APPROACH." International Journal of Reliability, Quality and Safety Engineering 13, no. 02 (2006): 171–91. http://dx.doi.org/10.1142/s0218539306002197.
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Occupational safety and illness surveillance has made a great effort to spread a "safety culture" to all workplaces and a great deal of progress has been made in finding solutions that guarantee safer working conditions.This paper analyses occupational injury data in order to identify specific risk groups and factors that in turn could be further analyzed to define prevention measures. A technique based on rule induction is put forward as a non-parametric alternative tool for analyzing occupational injury data which specifically uses the Classification And Regression Tree (CART) approach. Application of this technique to relevant work-related injury data collected in Italy has been encouraging. Data referring to 156 cases of injury in the period 2000–2002 were analyzed and lead to the factors that most affect work-related injuries being identified. According to the literature, up to the time of writing computer-intensive non-parametric modeling procedures have never been used to analyze occupational injuries. The aim of this paper is to use a real world application to illustrate the advantages and flexibility of applying a typical non-parametric epidemiological tool, such as CART, to an occupational injury study. This application can provide more informative, flexible, and attractive models identifying potential risk areas in support of decision-making in safety management.
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Lee, Dong-Hee, So-Hee Kim, Eun-Su Kim, Kwang-Jae Kim, and Zhen He. "MR-CART: Multiresponse optimization using a classification and regression tree method." Quality Engineering 33, no. 3 (2021): 457–73. http://dx.doi.org/10.1080/08982112.2021.1888120.
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Salimi, Alireza, Roohollah Shirani Faradonbeh, Masoud Monjezi, and Christian Moormann. "TBM performance estimation using a classification and regression tree (CART) technique." Bulletin of Engineering Geology and the Environment 77, no. 1 (2016): 429–40. http://dx.doi.org/10.1007/s10064-016-0969-0.
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Fitria Panca Ramadhani, Dodi Vionanda, Syafriandi Syafriandi, and Admi Salma. "Comparison of Error Rate Prediction Methods in Classification Modeling with Classification and Regression Tree (CART) Methods for Balanced Data." UNP Journal of Statistics and Data Science 1, no. 4 (2023): 271–79. http://dx.doi.org/10.24036/ujsds/vol1-iss4/73.
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CART (Classification and Regression Tree) is one of the classification algorithms in the decision tree method. The model formed in CART is a tree consisting of root nodes, internal nodes, and terminal nodes. After the model is formed, it is necessary to calculate the accuracy of the model. The aims is to see the performance of the model. The accuracy of this model can be done by calculating the predicted error rate in the model. The error rate prediction method works by dividing the data into training data and testing data. There are three methods in the error rate prediction method, such as Leave One Out Cross Validation (LOOCV), Hold Out (HO), and K-Fold Cross Validation. These methods have different performance in dividing data into training data and testing data, so there are advantages and disadvantages to each method. Therefore, a comparison was made for the three error rate prediction methods with the aim of determining the appropriate method for the CART algorithm. This comparison was made by considering several factors, for instance variations in the mean, number of variables, and correlations in normal distributed random data. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. The results of this study indicate that the K-Fold Cross Validation has the median error rate and the lowest variance, so the most suitable error prediction method used for the CART method is the K-Fold Cross Validation method.
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Lestawati, R., Rais Rais, and I. T. Utami. "PERBANDINGAN ANTARA METODE CART (CLASSIFICATION AND EGRESSION TREE) DAN REGRESI LOGISTIK (LOGISTIC REGRESSION) DALAM MENGKLASIFIKASIKAN PASIEN PENDERITA DBD (DEMAM BERDARAH DENGUE)." JURNAL ILMIAH MATEMATIKA DAN TERAPAN 15, no. 1 (2018): 98–107. http://dx.doi.org/10.22487/2540766x.2018.v15.i1.10206.
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Classification is one of statistical methods in grouping the data compiled systematically. The classification of an object can be done by two approaches, namely classification methods parametric and non-parametric methods. Non-parametric methods is used in this study is the method of CART to be compared to the classification result of the logistic regression as one of a parametric method. From accuracy classification table of CART method to classify the status of DHF patient into category of severe and non-severe exactly 76.3%, whereas the percentage of truth logistic regression was 76.7%, CART method to classify the status of DHF patient into categories of severe and non-severe exactly 76.3%, CART method yielded 4 significant variables that hepatomegaly, epitaksis, melena and diarrhea as well as the classification is divided into several segmens into a more accurate whereas the logistic regression produces only 1 significant variables that hepatomegaly
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Fernanda, J. W., G. Anuraga, and M. A. Fahmi. "Risk factor analysis of hypertension with logistic regression and Classification and Regression Tree (CART)." Journal of Physics: Conference Series 1217 (May 2019): 012109. http://dx.doi.org/10.1088/1742-6596/1217/1/012109.
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Imtiyaz, Widad, Neva Satyahadewi, and Hendra Perdana. "APPLICATION OF BAGGING CART IN THE CLASSIFICATION OF ON-TIME GRADUATION OF STUDENTS IN THE STATISTICS STUDY PROGRAM OF TANJUNGPURA UNIVERSITY." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 4 (2023): 2243–52. http://dx.doi.org/10.30598/barekengvol17iss4pp2243-2252.
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The timeliness of graduation is used as the success of students in pursuing education which can be seen from the time taken and measured by the predicate of graduation obtained. The characteristics of students who tend to graduate not or on time can be analyzed using classification techniques. Classification and Regression Tree (CART) is one of the classification tree methods. There is a weakness in CART, which is less stable in predicting a single classification tree. The weaknesses in CART can be improved by using Ensemble methods, one of which is Bootstrap Aggregating (Bagging) which can reduce classification errors and increase accuracy in a single classification model. This study aims to classify and determine the accuracy of Bagging CART in the case of the accuracy of student graduation classification. The number of samples used is 140 data on the graduation status of Untan Statistics Study Program students from Period I of the 2017/2018 academic year to Period II of the 2022/2023 academic year. The variables used are the timeliness of graduation which is categorized into two namely Not and On Time, Gender, Semester 1 GPA, Semester 2 GPA, Semester 3 GPA, Semester 4 GPA, Region of Origin Domicile, High School Accreditation, Entry Path, Scholarship, and first TUTEP. A good classification can be seen from the accuracy value. The CART method obtained an accuracy value of 70%. While using the CART Bagging method obtained an accuracy value of 85.71%. Based on the accuracy value obtained, the application of the CART Bagging method can increase accuracy and correct classification errors on a single CART classification tree by 15.71% by resampling 25 times.
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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms." Revue d'Intelligence Artificielle 35, no. 3 (2021): 209–15. http://dx.doi.org/10.18280/ria.350304.
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Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.
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Kumar, Sunil, Saroj Ratnoo, and Jyoti Vashishtha. "HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS." Journal of Information and Communication Technology 20, Number 2 (2021): 249–76. http://dx.doi.org/10.32890/jict2021.20.2.5.
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Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.
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Ningsih, Ningsih, Sutardi Sutardi, and Jumadil Nangi. "PENERAPAN METODE CLASSIFICATION AND REGRESSION TREE (CART) PADA SISTEM PREDIKSI MASA STUDI MAHASISWA." semanTIK 8, no. 2 (2022): 209. http://dx.doi.org/10.55679/semantik.v8i2.19385.
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Masa studi tepat waktu menjadi dambaan semua mahasiswa. Pihak universitas terus berupaya bagaimana meningkatkan jumlah mahasiswa yang lulus tepat waktu tanpa mengesampingkan factor kualitas. Penelitian ini bertujuan untuk mengelompokkan kelulusan mahasiswa pada Program Studi Teknik Informatika Universitas Halu Oleo. Metode yang digunakan adalah Metode Classification and Regression Trees (CART) untuk eksplorasi data dengan teknik pohon keputusan. Sistem yang dibangun akan menghitung data mahasiswa dan mengklasifikasikanya berdasarkan kelulusan apakah tepat waktu dan tidak tepat waktu. Berdasarkan hasil pengujian terhadap data latih sebesar 88,99% didapatkan nilai akurasi data uji sebesar 92,30%.Kata kunci; CART, Prediksi, Klasifikasi, Mahasiswa
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Aprihartha, Moch Anjas, and Idham Idham. "Optimization of Classification Algorithms Performance with k-Fold Cross Validation." EIGEN MATHEMATICS JOURNAL 7, no. 2 (2024): 61–66. http://dx.doi.org/10.29303/emj.v7i2.212.
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Supervised learning is a predictive method used to make predictions or classifications. Supervised learning algorithms work by building a model using training data that includes both independent and dependent variables. Several methods for building classification include Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), decision tree, etc. The lack of capacity of a classification algorithm to generalize certain data can be associated with the problem of overfitting or underfitting. K-fold cross-validation is a method that can help avoid overfitting or underfitting and produce a algorithm with good performance on new data. This study will test the Naive Bayes, K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Logistic Regression methods with k-fold cross-validation on two different datasets. The values of k set for cross-validation are 2, 3, 5, 7, and 10. The analysis results concluded that each classification algorithm performed best at 10-fold cross-validation. In DATA 1, the Naive Bayes algorithm has the highest average accuracy of 0.67 (67%) and the error rate is 0.33 (33%), followed by the CART algorithm, KNN, and finally logistic regression. While DATA 2, the KNN algorithm has the highest average accuracy of 0.66 (66%) and an error rate of 0.34 (34%), followed by the CART algorithm, Naive Bayes, and finally logistic regressionbut can be a reference if you want to predict the growth direction of the accommodation and food service activities sector.
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Fayza Annisa Febrianti, Dodi Vionanda, Yenni Kurniawati, and Fadhilah Fitri. "Emprical Study for Algorithms Comparison of Classification and Regression Tree and Logistic Regression Using Combined 5×2cv F Test." UNP Journal of Statistics and Data Science 1, no. 4 (2023): 353–60. http://dx.doi.org/10.24036/ujsds/vol1-iss4/85.
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Classification is a method to estimate the class of an object based on its characteristics. Several learning algorithms can be applied in classification, such as Classification and Regression Tree (CART) and logistic regression. The main goal of classification is to find the best learning algorithm that can be applied to get the best classifier. In comparing two learning algorithms, a direct comparison by seeing the smaller prediction error rate may be possible when the difference is very clear. In this case, direct comparison is misleading and resulting inadequate conclusions. Therefore, a statistical test is needed to determine whether the difference is real or random. The results of the 5×2cv paired t-test sometimes reject and sometimes fail to reject the hypothesis. It is distracting because the changing of the error rate difference should not affect the test result. Meanwhile, the overall results of the combined 5×2cv F test show that the tests fail to reject the hypothesis. This indicates that CART and logistic regression perform identically in this case.
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Nagata, T., D. Mattern, R. Schmelzeisen, M. Schumacher, and G. Schwarzer. "Comparison of Fuzzy Inference, Logistic Regression, and Classification Trees (CART)." Methods of Information in Medicine 42, no. 05 (2003): 572–77. http://dx.doi.org/10.1055/s-0038-1634385.
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Summary Objectives: In this paper three statistical methods [logistic regression, classification and regression tree (CART), and fuzzy inference] for the prediction of lymph node metastasis in carcinoma of the tongue are compared. Methods: A retrospective collection of data in 75 patients treated for tongue cancer was carried out at the Clinic and Policlinic for Oral and Maxillo-facial Surgery at the University Hospital of Freiburg in Germany between January 1990 and December 1999; biopsy material was used for laboratory evaluations. Statistical methods for the prediction of lymph node metastasis were compared using ROC curves and accuracy rates. Results: All three methods show similar results for the prediction of lymph node metastasis with slightly superior results for fuzzy inference and CART. A great overlap is apparent in the ROC curves. The best result observed for fuzzy inference and CART was a sensitivity of 79.2% [95% confidence interval: (57.8%; 92.9%)] and a specificity of 86.3% (73.7%; 94.3%); the best result for predictions based on the logistic regression was a sensitivity of 66.7% (44.7%; 84.4%) and a specificity of 80.4% (66.9%; 90.2%). Accuracy rates of fuzzy method and CART were higher [accuracy rate for fuzzy method and CART: 84% (73.7%; 91.4%), for logistic regression method: 73.3%, 95%-CI: (61.9%; 82.9%)]. Conclusions: From a clinical point of view, the predictive ability of the three methods is not sufficiently large to justify use of these methods in daily practice. Other factors probably on the molecular level are needed for the prediction of lymph node metastasis.
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Samat, Alim, Erzhu Li, Wei Wang, Sicong Liu, Cong Lin, and Jilili Abuduwaili. "Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles." Remote Sensing 12, no. 12 (2020): 1973. http://dx.doi.org/10.3390/rs12121973.
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To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.
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Alamsyah, Agung, Anita Desiani, and Endro Setyo Cahyono. "Klasifikasi Gejala Awal Covid-19 dengan Algoritma Classification and Regression Tree (Cart)." KOMPUTEK 7, no. 2 (2023): 67–76. https://doi.org/10.24269/jkt.v7i2.2095.
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COVID-19 is a disease that can cause death and can spread to others. By identifying early symptoms of the disease, early detection can be made for several symptoms that may cause COVID-19. One way to predict COVID-19 is through classification methods. By identifying the symptoms that have an impact on COVID-19, it is hoped that the COVID-19 virus can be stopped from spreading and the world's condition can be normal. This study shows an analysis of attributes that may have an impact on the onset of COVID-19 in an individual. The classification method used is one of the decision tree methods, namely the Classification and Regression Tree (CART). The training and testing methods used in this study are cross-validation and percentage split. The attribute that has a significant influence in this classification using CART method is lung infection. The performance of the system using cross-validation method with a value of k of 10 obtained an accuracy of 85%, which is considered good, while using a percentage split of 66%, an accuracy of 87% was obtained. The evaluation results for the class indicating COVID-19 with precision and recall in cross-validation are 70% and 68%, respectively, while for the percentage split method, precision and recall values of 75% and 70% were obtained, respectively.