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Journal articles on the topic 'Decision Tree Algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Parlindungan and HariSupriadi. "Implementation Decision Tree Algorithm for Ecommerce Website." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3611–14. http://dx.doi.org/10.37200/ijpr/v24i2/pr200682.

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Babar, Kiran Nitin. "Performance Evaluation of Decision Trees with Machine Learning Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34179.

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Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. Decision trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition and Data Mining have dealt with the issue of growing a decision tree from available data. Decision trees in machine learning will be used for classification problems, to categorize objects to gain an understanding of similar features. Decision trees helps in decision-making by representing co

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Tetteh, Evans Teiko, and Beata Zielosko. "Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles." Entropy 27, no. 1 (2025): 35. https://doi.org/10.3390/e27010035.

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This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules th

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Xu, Yuwei. "Comparative study on early risk warning for corporate bond defaults." Intelligent Decision Technologies 19, no. 3 (2025): 1675–82. https://doi.org/10.1177/18724981241309549.

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Accurate prediction of default risk in corporate bonds is essential for maintaining market stability. This paper briefly introduces the decision tree and random forest (RF) algorithms. The RF algorithm was applied to predict default risk in corporate bonds. Moreover, simulation experiments were conducted. Firstly, the feature indices’ effectiveness for predicting default risk in corporate bonds was verified. Secondly, the impact of the number of decision trees on the RF algorithm was tested. Finally, the performance of the support vector machine (SVM), decision tree, back-propagation neural ne

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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.

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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 spl

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PURDILA, V., and S. G. PENTIUC. "Fast Decision Tree Algorithm." Advances in Electrical and Computer Engineering 14, no. 1 (2014): 65–68. http://dx.doi.org/10.4316/aece.2014.01010.

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Hajjej, Fahima, Manal Abdullah Alohali, Malek Badr, and Md Adnan Rahman. "A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data." BioMed Research International 2022 (July 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/9449497.

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By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and

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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 met

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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 pa

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Sullivan, Colin, Mo Tiwari, and Sebastian Thrun. "MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9019–26. http://dx.doi.org/10.1609/aaai.v38i8.28751.

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Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees. We first demonstrate a connection between maximum a posteriori inference of decision trees and AND/OR search. Using this connection, we propose an AND/OR search algorithm, dubbed MAPTree, which is able to recover the maximum a posteriori tree. Lastly, we demonstrate the empirical performance of the maximu

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Pathan, Shabana, and Sanjeev Kumar Sharma. "Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (2023): 127–33. http://dx.doi.org/10.17762/ijritcc.v11i6.7295.

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Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for c

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Maulana, Bima, Dany Febrian, Irgie Rachmat Fachrezi, and Muhammad Ferdi Zeen. "Comparison of Support Vector Machine, Random Forest, and C4.5 Algorithms for Customer Loss Prediction." IJATIS: Indonesian Journal of Applied Technology and Innovation Science 2, no. 1 (2025): 1–6. https://doi.org/10.57152/ijatis.v2i1.1102.

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Loss of customers has been discussed and many studies have been conducted, starting from using the Bayesian network algorithm, Decision tree, random vorest, Support vector machine, and neyral network Algorithms Support Vector Machine (SVM), Random Forest, and Decision Tree or C4.5 are algorithms used for prediction and have several advantages Random forest has the advantage of being able to combine many predictions from decision trees that have a tendency to reduce overfitting. This research uses the C4.5 algorithm, SVM and random forest. Research shows that the Random Forest method has the hi

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Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2879–86. http://dx.doi.org/10.24297/ijct.v11i8.3006.

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In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth.Â Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate

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VUKIĆEVIĆ, MILAN, MILOŠ JOVANOVIĆ, BORIS DELIBAŠIĆ, SONJA IŠLJAMOVIĆ, and MILIJA SUKNOVIĆ. "REUSABLE COMPONENT-BASED ARCHITECTURE FOR DECISION TREE ALGORITHM DESIGN." International Journal on Artificial Intelligence Tools 21, no. 05 (2012): 1250022. http://dx.doi.org/10.1142/s0218213012500224.

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Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been

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Avcı, Keziban. "Evaluation of Public Hospitals' Performance with Decision Tree Algorithms." Verimlilik Dergisi 59, no. 1 (2025): 133–42. https://doi.org/10.51551/verimlilik.1494277.

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Purpose: The study aims to evaluate a range of financial performance indicators calculated through structural, operational, and HVI measures for public hospitals in the Turkish healthcare sector using various decision tree algorithms. Methodology: The study comprises threa phases. In the first phase, financial ratios were calculated from the hospitals' financial statements using the ratio analysis method. In the second phase, these ratios were used to calculate the HVI. In the third phase, the selected operational and financial indicators were analyzed with decision tree algorithms. The ID3, C

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Go, Eunby, Seungmin Lee, and Taeseon Yoon. "Analysis of Ebolavirus with Decision Tree and Apriori algorithm." International Journal of Machine Learning and Computing 4, no. 6 (2014): 543–46. http://dx.doi.org/10.7763/ijmlc.2014.v6.470.

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Kirandeep, Kirandeep, and Prof Neena Madan. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 740–44. http://dx.doi.org/10.31142/ijtsrd11073.

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Zaheer, M. M., and P. Nirmala. "An Effective Approach to Detect Liver Disorder using KNN Algorithm in Comparison with Decision Tree Algorithm to Measure Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 1038–46. http://dx.doi.org/10.18137/cardiometry.2022.25/10381046.

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Aim: An effective approach to detect liver disorder using KNN algorithm in comparison with decision tree algorithm to measure accuracy. Methods and Materials: For identification of effective approaches to detect liver disorder, the KNN algorithm is used comparatively with the decision tree algorithm which is an existing algorithm. The number of samples used in each group is 20. Result: It is discovered that the K- nearest neighbor (KNN) algorithm gives better mean accuracy than the decision tree algorithm. Each sample has a distinct level of accuracy, with KNN algorithm having a maximum accura

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Yang, Bin-Bin, Song-Qing Shen, and Wei Gao. "Weighted Oblique Decision Trees." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5621–27. http://dx.doi.org/10.1609/aaai.v33i01.33015621.

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Decision trees have attracted much attention during the past decades. Previous decision trees include axis-parallel and oblique decision trees; both of them try to find the best splits via exhaustive search or heuristic algorithms in each iteration. Oblique decision trees generally simplify tree structure and take better performance, but are always accompanied with higher computation, as well as the initialization with the best axis-parallel splits. This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization. We consider different we

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Chandra, B., and P. P. Varghese. "Fuzzy SLIQ Decision Tree Algorithm." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (2008): 1294–301. http://dx.doi.org/10.1109/tsmcb.2008.923529.

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Priyanka, Saini. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 1, April (2021): 31–41. https://doi.org/10.5281/zenodo.5105645.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Priyanka, Saini1 Sweta Rai2 and Ajit Kumar Jain3 1. 2. M.Tech Student Banasthali University Tonk Rajasthan. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 01, dec (2014): 01–11. https://doi.org/10.5281/zenodo.1450276.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Zaheer, M. M., and P. Nirmala. "An Efficient Approach to Detect Liver Disorder Using Naive Bayes in Comparison with Decision Tree Algorithm to Measure Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 1047–53. http://dx.doi.org/10.18137/cardiometry.2022.25.10471053.

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Aim: To process an effective approach to detect liver disorder using Naive Bayes algorithm in comparison with Decision tree algorithm to measure accuracy. Methods & Materials: There are 20 samples used for both groups, where group 1 is Naive Bayes algorithm and group 2 is Decision tree algorithm which are effectively used for the identification of liver disorder approach. Result: It is a novel detection method, and it has been discovered that the Naive Bayes algorithm performs better than the Decision tree algorithm. Each sample has a distinct level of accuracy, with Naive Bayes having a m

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Zhang, Hui. "The Analysis of English Sentence Components Based on Decision Tree Classification Algorithm." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 317–20. http://dx.doi.org/10.54097/hset.v23i.3617.

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Decision tree is an important classification method in data excavation technology. It is a predictive analysis model expressed in the form of a tree structure (including binary trees and poly trees). The decision tree method is a more general classification function approximation method. It is an algorithm commonly used in predictive models to find some potentially valuable information by purposefully classifying a large amount of data. In this article, the author tries to analyze the English sentence components based on the decision tree classification algorithm. The author starts with the de

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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 consider

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Bansal, Ankita, and Sourabh Jajoria. "Cross-Project Change Prediction Using Meta-Heuristic Techniques." International Journal of Applied Metaheuristic Computing 10, no. 1 (2019): 43–61. http://dx.doi.org/10.4018/ijamc.2019010103.

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Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and obliqu

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Sinlae, Fried, Anugrah Sandy Yudhasti, and Arief Wibowo. "Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity." Journal of Systems Engineering and Information Technology (JOSEIT) 1, no. 2 (2022): 47–51. http://dx.doi.org/10.29207/joseit.v1i2.3439.

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The level of data accuracy in everyday life is necessary because it is reflected in the ever-advancing development of information technology. Analysis of data processing in information that can provide knowledge with the help of data mining systems. Algorithms commonly used for prediction are Naive Bayes and Decision Trees. The purpose of this study is to compare the Nave-Bayes algorithm and the decision tree algorithm in terms of the accuracy of predicting the productivity of the Weckerle machine at PT XYZ. The method used is a literature study from various related sources and understanding o

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Yuliawan, Kristia, and Stevanus Murib. "COMPARISON OF DECISION TREE AND NAÏVE BAYES ALGORITHMS IN PREDICTING STUDENT GRADUATION AT YPK JUNIOR HIGH SCHOOL, NABIRE REGENCY." JIKO (Jurnal Informatika dan Komputer) 7, no. 2 (2024): 117–22. http://dx.doi.org/10.33387/jiko.v7i2.8506.

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This study aims to compare the accuracy of the Decision Tree C4.5 and Naive Bayes algorithms in predicting student graduation at YPK Immanuel Nabire Junior High School, Central Papua. Student data from the 2022 and 2023 school years were used as training data, whereas student data for the 2024 school year were used as testing data. Data collection methods included field studies, interviews with schools, and literature studies. The implementation of the algorithm is carried out using the Orange software, which simplifies the process of data visualization and analysis. Both algorithms are applie

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Pogorilyy, S. D., and P. V. Biletskyi. "Coreference resolution algorithm for Ukrainian-language texts using decision trees." PROBLEMS IN PROGRAMMING, no. 3-4 (December 2022): 85–91. http://dx.doi.org/10.15407/pp2022.03-04.085.

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The paper examines the problem of coreference resolution in Ukrainian-language texts using decision trees. An application that uses vector representations of Elmo words and other characteristics for the automated formation of a decision tree has been developed. A set of prepared texts containing more than 360,000 words was used to form the decision tree and evaluate the accuracy of the algorithm. The decision tree created to determine whether a pair of objects is coreference was used to form clusters of coreference objects. Special metrics were used for comparison with the results obtained by

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Khoshgoftaar, Taghi M., and Naeem Seliya. "Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm." International Journal on Artificial Intelligence Tools 12, no. 03 (2003): 207–25. http://dx.doi.org/10.1142/s0218213003001204.

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Predicting the quality of system modules prior to software testing and operations can benefit the software development team. Such a timely reliability estimation can be used to direct cost-effective quality improvement efforts to the high-risk modules. Tree-based software quality classification models based on software metrics are used to predict whether a software module is fault-prone or not fault-prone. They are white box quality estimation models with good accuracy, and are simple and easy to interpret. An in-depth study of calibrating classification trees for software quality estimation u

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Shan, Gui Jun. "Partition Real Data in Decision Tree Using Statistical Criterion." Applied Mechanics and Materials 380-384 (August 2013): 1469–72. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1469.

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Partition methods for real data play an extremely important role in decision tree algorithms in data mining and machine learning because the decision tree algorithms require that the values of attributes are discrete. In this paper, we propose a novel partition method for real data in decision tree using statistical criterion. This method constructs a statistical criterion to find accurate merging intervals. In addition, we present a heuristic partition algorithm to achieve a desired partition result with the aim to improve the performance of decision tree algorithms. Empirical experiments on

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García-Martín, Eva, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, and Veselka Boeva. "Energy-aware very fast decision tree." International Journal of Data Science and Analytics 11, no. 2 (2021): 105–26. http://dx.doi.org/10.1007/s41060-021-00246-4.

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AbstractRecently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting

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Gao, Jing. "Decision Tree Generation Algorithm without Pruning." Applied Mechanics and Materials 441 (December 2013): 731–37. http://dx.doi.org/10.4028/www.scientific.net/amm.441.731.

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On the generation of decision tree based on rough set model, for the sake of classification accuracy, existing algorithms usually partition examples too specific. And it is hard to avoid the negative impact caused by few special examples on decision tree. In order to obtain this priority in traditional decision tree algorithm based on rough set, the sample is partitioned much more meticulously. Inevitably, a few exceptional samples have negative effect on decision tree. And this leads that the generated decision tree seems too large to be understood. It also reduces the ability in classifying

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Jun, Sungbum. "Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing." Sensors 21, no. 8 (2021): 2849. http://dx.doi.org/10.3390/s21082849.

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Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining

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Balaji, Arun, and V. Sugumaran. "Robust algorithm to learn rules for classification: A fault diagnosis case study." FME Transactions 51, no. 3 (2023): 338–46. http://dx.doi.org/10.5937/fme2303338b.

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Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning. The optimum hyperparameter values are obtained using either optimization algorithms or trial and error methods. The present study utilizes the MODLEM algorithm to overcome the drawbacks accounted for by decision tree algorithms. Eliminating hyperparameter tuning and producing results closer to standard decision tree algorithms makes MODLEM a robust classification algorithm. The

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Mitrofanov, S. A., and E. S. Semenkin. "Tree retraining in the decision tree learning algorithm." IOP Conference Series: Materials Science and Engineering 1047, no. 1 (2021): 012082. http://dx.doi.org/10.1088/1757-899x/1047/1/012082.

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Suyadi, Suyadi, Arief Setyanto, and Hanif Al Fattah. "Analisis Perbandingan Algoritma Decision Tree (C4.5) Dan K-Naive Bayes Untuk Mengklasifikasi Penerimaan Mahasiswa Baru Tingkat Universitas." Indonesian Journal of Applied Informatics 2, no. 1 (2017): 59. http://dx.doi.org/10.20961/ijai.v2i1.13258.

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<em>Profile of PMB (New Student Admissions) students from several periods have abundant data that can be used for research. The data is in the form of student information from the majors of origin, NEM and majors now. Classifying the PMB profile data of students at the University level in Yogyakarta can know the majority of learners. Comparing some algorithms is needed to find out the best algorithm. Classification is a grouping algorithm that has several algorithms such as Decision Tree (C4.5) and K-Naive Bayes. Decision Tree (C4.5) is an algorithm with decision tree, while K-Naive Baye

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Hafeez, Muhammad Asfand, Muhammad Rashid, Hassan Tariq, Zain Ul Abideen, Saud S. Alotaibi, and Mohammed H. Sinky. "Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm." Applied Sciences 11, no. 15 (2021): 6728. http://dx.doi.org/10.3390/app11156728.

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Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf

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Liu, Biao, and Zhipeng Sun. "Global Economic Market Forecast and Decision System for IoT and Machine Learning." Mobile Information Systems 2022 (April 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/8344791.

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The fast growth of IoT in wearable devices, smart sensors, and home appliances will affect every aspect of our lives. With the rapid development of economic globalization, how to integrate science and technology into economic decision-making is the focus of the current research field, and the research of this paper is precisely to solve this problem. This paper proposes a global economic market forecasting and decision-making system research based on the Internet of Things and machine learning. Using the wireless sensor network of the Internet of Things technology to perceive and predict the g

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Chen, Yihang, Shuoyu Chen, Yicheng Yang, and Siming Lu. "Comparison of decision tree and ensemble algorithms." Applied and Computational Engineering 55, no. 1 (2024): 241–48. http://dx.doi.org/10.54254/2755-2721/55/20241535.

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This paper presents an in-depth exploration of the Adaboost algorithm in the context of machine learning, focusing on its application in classification tasks. Adaboost, known for its adaptive boosting approach, is examined for its ability to enhance weak learners, particularly decision tree classifiers. The study delves into the theoretical underpinnings of Adaboost, emphasizing its iterative process for minimizing the exponential loss function. The role of decision trees, as integral components of this algorithm, is analyzed in detail. These trees, with their hierarchical query structure, are

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Guo, Jinlin, Haoran Wang, Xinwei Li, and Li Zhang. "Stream Classification Algorithm Based on Decision Tree." Mobile Information Systems 2021 (December 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/3103053.

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Due to the rise of many fields such as e-commerce platforms, a large number of stream data has emerged. The incomplete labeling problem and concept drift problem of these data pose a huge challenge to the existing stream data classification methods. In this respect, a dynamic stream data classification algorithm is proposed for the stream data. For the incomplete labeling problem, this method introduces randomization and iterative strategy based on the very fast decision tree VFDT algorithm to design an iterative integration algorithm, and the algorithm uses the previous model classification r

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LAST, MARK, ODED MAIMON, and EINAT MINKOV. "IMPROVING STABILITY OF DECISION TREES." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 02 (2002): 145–59. http://dx.doi.org/10.1142/s0218001402001599.

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Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. Both accuracy and stability can be improved by learning multiple models from bootstrap samples of training data, but the "meta-learner" approach makes the extracted knowledge hardly interpretable. In the following paper, we present the Info-Fuzzy Network (IFN), a novel information-theoretic method for building stable and comprehensible decision-tree models. The stability of the IFN algorithm is ensured by restricting t

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Hendri, Desvita, Diana Nadha, Faishal Khairi Basri, Muhammad Farid Wajdi, and Nurul Nadhirah. "Comparation of Decision Tree Algorithm, Naive Bayes, K-Nearest Neighbords on Spotify Music Genre." IJATIS: Indonesian Journal of Applied Technology and Innovation Science 1, no. 1 (2024): 47–53. http://dx.doi.org/10.57152/ijatis.v1i1.1219.

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Comparison of Decision Tree, Naive Bayes, K-Nearest Neighbords Algorithm on Spotify Music Genre Decision Tree, Naive Bayes, K-Nearest Neighbords This research aims to compare three algorithms Decision Tree, Naive Bayes and K-Nearest Neighbors (K-NN) in classifying Spotify music genres using dataset from Kaggle. The results show that the Decision Tree algorithm produces an accuracy of 23%, Naive Bayes 17%, and K-Nearest Neighbors 19%. This research provides an overview of Spotify music listeners in choosing music genres. Based on research results, the Decision Tree algorithm has the highest acc

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Mazraeh, Saeed, Maryam Ghanavati, and Sajedeh Hasan Nejad Neysi. "Intrusion detection system with decision tree and combine method algorithm." International Academic Journal of Science and Engineering 06, no. 01 (2019): 167–77. http://dx.doi.org/10.9756/iajse/v6i1/1910016.

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Kenechukwu, Nwosu Ifeanyi, Kene Tochukwu Anyachebelu, and Muhammad, Umar Abdullahi. "Detection of Fraudulent Health Insurance Claims Based on Decision Tree with Principal Component Analysis." Asian Journal of Research in Computer Science 16, no. 4 (2023): 49–66. http://dx.doi.org/10.9734/ajrcos/2023/v16i4370.

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Fraudulent health insurance claims pose a significant challenge to insurance companies and healthcare providers, leading to substantial financial losses and compromised service quality. In this study, we focused on detecting fraudulent health insurance claims using the decision tree algorithm and principal component analysis (PCA). The objective was to gain valuable insights and extract meaningful patterns from the dataset to enhance fraud detection capabilities. We developed a comprehensive method that employed the decision tree algorithm to build a decision tree-based model and the PCA for d

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Ã–zsoy, Salih, GÃ¶khan GÃ¼mÃ¼ÅŸ, and Savriddin KHALILOV. "C4.5 Versus Other Decision Trees: A Review." Computer Engineering and Applications Journal 4, no. 3 (2015): 173–82. http://dx.doi.org/10.18495/comengapp.v4i3.141.

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In this study, Data Mining, one of the latest technologies of the Information Systems, was introduced and Classification a Data Mining method and the Classification algorithms were discussed. A classification was applied by using C4.5 decision tree algorithm on a dataset about Labor Relations from http://archive.ics.uci.edu/ml/datasets.html. Finally, C4.5 algorithm was compared to some other decision tree algorithms. C4.5 was the one of the successful classifier.

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Sameer, S. K. L., and P. Sriramya. "Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 713–20. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21100.

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Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations

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Saputri, Daniati Uki Eka, Fitra Septia Nugraha, Taopik Hidayat, Abdul Latif, Ade Suryadi, and Achmad Baroqah Pohan. "IMPLEMENTATION OF DECISION TREE AND K-NN CLASSIFICATION OF INTEREST IN CONTINUING STUDENT SCHOOL." Jurnal Techno Nusa Mandiri 17, no. 1 (2020): 51–56. http://dx.doi.org/10.33480/techno.v17i1.1289.

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Education is important to prepare quality Human Resources (HR) because quality human resources is an important factor for the nation and state development. Therefore, it is expected that every citizen has the right to get high educational opportunities from the 12-year compulsory education level. This study aims to implement the Decision Tree and K-NN algorithm in the classification of student interest in continuing school. This study proposes combining the Decision Tree and K-NN algorithm methods to improve accuracy with the Gain Ratio, Information Gain and Gini Index approaches for the measu

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Lafond, Daniel, Benoît Roberge-Vallières, François Vachon, and Sébastien Tremblay. "Judgment Analysis in a Dynamic Multitask Environment: Capturing Nonlinear Policies Using Decision Trees." Journal of Cognitive Engineering and Decision Making 11, no. 2 (2016): 122–35. http://dx.doi.org/10.1177/1555343416661889.

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Policy capturing is a judgment analysis method that typically uses linear statistical modeling to estimate expert judgments. A variant to this technique is to capture decision policies using data-mining algorithms designed to handle nonlinear decision rules, missing attributes, and noisy data. In the current study, we tested the effectiveness of a decision-tree induction algorithm and an instance-based classification method for policy capturing in comparison to the standard linear approach. Decision trees are relevant in naturalistic decision-making contexts since they can be used to represent

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