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Journal articles on the topic '"decision tree" model'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Guidotti, Riccardo, Anna Monreale, Mattia Setzu, and Giulia Volpi. "Generative Model for Decision Trees." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21116–24. http://dx.doi.org/10.1609/aaai.v38i19.30104.

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Decision trees are among the most popular supervised models due to their interpretability and knowledge representation resembling human reasoning. Commonly-used decision tree induction algorithms are based on greedy top-down strategies. Although these approaches are known to be an efficient heuristic, the resulting trees are only locally optimal and tend to have overly complex structures. On the other hand, optimal decision tree algorithms attempt to create an entire decision tree at once to achieve global optimality. We place our proposal between these approaches by designing a generative mod

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Naveen Kumar, Nallamothu. "Model of Decision Tree for Email Classification." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1502–5. http://dx.doi.org/10.21275/sr22722110223.

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Muhammad Sani, Anas, Ahmad Salihu BenMusa, and Muhammad Haladu. "In-Depth Study of Decision Tree Model." International Journal of Science and Research (IJSR) 10, no. 11 (2021): 705–9. https://doi.org/10.21275/mr211102051237.

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Kang, Donggil, WenXing Yu, and HyungJun Cho. "Decision Tree for Mode Estimation." Korean Data Analysis Society 25, no. 3 (2023): 903–11. http://dx.doi.org/10.37727/jkdas.2023.25.3.903.

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Decision trees are one of the data mining techniques that make predictions by recursively partitioning data structures based on split rules. Since the analysis results can be understood through the tree structure, it has the advantage of having high interpretation power as well as predictive power. In addition, it is used in many fields because it is able to identify nonlinear relationships between response and predictor variables. However, if the purpose of it is to predict the mode of the response variable, there is a limitation in that the previously proposed decision tree cannot be applied

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Tsehay, Admassu Assegie, Kumar Napa Komal, Thulasi Thiyagu, Kalyan Kumar Angati, Jeyanthiran Thiruvarasu Vasantha Priya Maran, and Dhamodaran Vigneswari. "Scalability and performance of decision tree for cardiovascular disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2540–45. https://doi.org/10.11591/ijai.v13.i3.pp2540-2545.

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As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the perf

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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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Jeon, Youtaek, and HyungJun Cho. "Model based hybrid decision tree." Journal of the Korean Data And Information Science Society 30, no. 3 (2019): 515–24. http://dx.doi.org/10.7465/jkdi.2019.30.3.515.

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Li, Jiawei, Yiming Li, Xingchun Xiang, Shu-Tao Xia, Siyi Dong, and Yun Cai. "TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation." Entropy 22, no. 11 (2020): 1203. http://dx.doi.org/10.3390/e22111203.

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Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at differ

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Susdarwono, Endro Tri, and Ananda Setiawan. "PENERAPAN TEORI KEPUTUSAN DALAM MODEL PENGAMBILAN KEPUTUSAN TERKAIT MASALAH EKONOMI PERTAHANAN." Jurnal Ilmu Manajemen dan Akuntansi Terapan (JIMAT) 11, no. 2 (2020): 258–70. http://dx.doi.org/10.36694/jimat.v11i2.239.

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The shift in global paradigm and threat perspective has led to a wide variety of possible risks and uncertainties. This situation also occurs in the defense economy, so understanding the basic principles of risk and uncertainty is important, especially in a decision-making process. There are several elements and concepts that are usually used in all decision models. Almost all models, whether complex or simple, can be formulated using a standard structure and solved by using general evaluation procedures. For decisions involving a series of decisions and relating to various basic sequential co

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Dobashi, Nao, Shota Saito, Yuta Nakahara, and Toshiyasu Matsushima. "Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction." Entropy 23, no. 6 (2021): 768. http://dx.doi.org/10.3390/e23060768.

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This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees

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Rautenberg, Tamlyn, Annette Gerritsen, and Martin Downes. "Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer." Diagnostics 10, no. 3 (2020): 158. http://dx.doi.org/10.3390/diagnostics10030158.

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Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the stru

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Suljic, Mirza, Edin Osmanbegovic, and Željko Dobrović. "Common Metamodel of Questionnaire Model and Decision Tree Model." Research in Applied Economics 10, no. 3 (2018): 106. http://dx.doi.org/10.5296/rae.v10i3.13540.

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The subject of this paper is metamodeling and its application in the field of scientific research. The main goal is to explore the possibilities of integration of two methods: questionnaires and decision trees. The questionnaire method was established as one of the methods for data collecting, while the decision tree method represents an alternative way of presenting and analyzing decision making situations. These two methods are not completely independent, but on the contrary, there is a strong natural bond between them. Therefore, the result reveals a common meta-model that over common conce

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Luna, José Marcio, Efstathios D. Gennatas, Lyle H. Ungar, et al. "Building more accurate decision trees with the additive tree." Proceedings of the National Academy of Sciences 116, no. 40 (2019): 19887–93. http://dx.doi.org/10.1073/pnas.1816748116.

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The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as tho

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Admassu Assegie, Tsehay, Komal Kumar Napa, Thiyagu Thulasi, Angati Kalyan Kumar, Maran Jeyanthiran Thiruvarasu Vasantha Priya, and Vigneswari Dhamodaran. "Scalability and performance of decision tree for cardiovascular disease prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2540. http://dx.doi.org/10.11591/ijai.v13.i3.pp2540-2545.

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<span lang="EN-US">As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The

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Kim, Kyunghun. "An Analysis of Global House Price using Decision Tree Model." JOURNAL OF SOCIAL SCIENCE 29, no. 1 (2022): 107–22. http://dx.doi.org/10.46415/jss.2022.03.29.1.107.

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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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Arkin, Esther M., Henk Meijer, Joseph S. B. Mitchell, David Rappaport, and Steven S. Skiena. "Decision Trees for Geometric Models." International Journal of Computational Geometry & Applications 08, no. 03 (1998): 343–63. http://dx.doi.org/10.1142/s0218195998000175.

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A fundamental problem in model-based computer vision is that of identifying which of a given set of geometric models is present in an image. Considering a "probe" to be an oracle that tells us whether or not a model is present at a given point, we study the problem of computing efficient strategies ("decision trees") for probing an image, with the goal to minimize the number of probes necessary (in the worst case) to determine which single model is present. We show that a ⌈l g k⌉ height binary decision tree always exists for k polygonal models (in fixed position), provided (1) they are non-deg

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AMPUŁA, Dariusz. "Prediction of Post-Diagnostic Decisions for Tested Hand Grenades’ Fuzes Using Decision Trees." Problems of Mechatronics Armament Aviation Safety Engineering 12, no. 2 (2021): 39–54. http://dx.doi.org/10.5604/01.3001.0014.9332.

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The article presents a brief history of creation of decision trees and defines the purpose of the undertaken works. The process of building a classification tree, according to the CHAID method, is shown paying particular attention to the disadvantages, advantages, and characteristics features of this method, as well as to the formal requirements that are necessary to build this model. The tree’s building method for UZRGM (Universal Modernised Fuze of Hand Grenades) fuzes was characterized, specifying the features of the tested hand grenade fuzes and the predictors used that are necessary to cr

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Azwar, Nurul Azri, Parapat Gultom, and Sawaluddin Sawaluddin. "Discrete Optimization Model in Constructing Optimal Decision Tree." SinkrOn 7, no. 3 (2022): 2108–15. http://dx.doi.org/10.33395/sinkron.v7i3.11592.

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Decision trees have been well studied and widely used in knowledge discovery and decision support systems. One of the applications of binary integer programming to form decision trees or decision making is the knapsack problem. The knapsack problem is an integer programming problem that involves only one constraint. The knapsack problem is generally illustrated with a bag and an item. The problem to be solved is to maximize the price of goods with a certain capacity that can be loaded by a bag with a certain capacity too. In solving the knapsack problem, it can generally be done directly. In t

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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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PETERSON, ADAM H., and TONY R. MARTINEZ. "REDUCING DECISION TREE ENSEMBLE SIZE USING PARALLEL DECISION DAGS." International Journal on Artificial Intelligence Tools 18, no. 04 (2009): 613–20. http://dx.doi.org/10.1142/s0218213009000305.

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This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encoding an ensemble, the new model behaves similar to the original ensemble, and can be made to perform identically to it. The reduced storage requirements allow an ensemble approach to be used in cases where

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Aisyah, Siti. "LOAN STATUS PREDICTION USING DECISION TREE CLASSIFIER." Power Elektronik : Jurnal Orang Elektro 13, no. 1 (2024): 68–70. http://dx.doi.org/10.30591/polektro.v12i3.6591.

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This paper investigates the effectiveness of the Decision Tree Classifier in predicting loan status, a critical task in the financial sector. The study utilizes a dataset containing various attributes of loan applicants such as income, credit score, employment status, and loan amount. The dataset is preprocessed to handle missing values and categorical variables. Feature importance is analyzed to understand the key factors influencing loan approval decisions. A Decision Tree Classifier model is trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. T

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Nurkholis, Andi, Imas Sukaesih Sitanggang, Annisa Annisa, and Sobir Sobir. "Spatial decision tree model for garlic land suitability evaluation." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 666. http://dx.doi.org/10.11591/ijai.v10.i3.pp666-675.

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Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok di

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Andi, Nurkholis, Sukaesih Sitanggang Imas, Annisa, and Sobir. "Spatial decision tree model for garlic land suitability evaluation." International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 666–75. https://doi.org/10.11591/ijai.v10.i3.pp666-675.

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Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok di

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Konstantinov, Andrei, Lev Utkin, and Vladimir Muliukha. "Multiple Instance Learning with Trainable Soft Decision Tree Ensembles." Algorithms 16, no. 8 (2023): 358. http://dx.doi.org/10.3390/a16080358.

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A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed. A new type of soft decision trees is considered, which is similar to the well-known soft oblique trees, but with a smaller number of trainable parameters. In order to train the trees, it is proposed to convert them into neural networks of a specific form, which approximate the tree functions. It is also proposed to aggregate the instance and bag embeddings (output vectors) by using the attention mechanism. The whole

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Mohammad, Khanbabaei, and Alborzi Mahmood. "The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring." International Journal of Managing Information Technology (IJMIT) 5, no. 4 (2013): 13 to 32. https://doi.org/10.5281/zenodo.3828274.

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Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model. It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting appropriate features and building optimum decision trees. The new propo

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I., Dwaraka Srihith, Vijaya Lakshmi P., David Donald A., Aditya Sai Srinivas T., and Thippanna G. "A Forest of Possibilities: Decision Trees and Beyond." Journal of Advancement in Parallel Computing 6, no. 3 (2023): 29–37. https://doi.org/10.5281/zenodo.8372196.

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<em>Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ensemble methods like Random Forests. Additionally, it covers handling categorical data, imbalanced datasets, missing values, and hyperparameter tuning. Decision trees are valuable for feature selection and model interpretability. However, they have

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Ampuła, Dariusz. "Decision Trees in the Tests of Artillery Igniters." Journal of KONBiN 50, no. 1 (2020): 95–116. http://dx.doi.org/10.2478/jok-2020-0007.

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AbstractThe article addressed the method for building decision trees paying attention to the binary character of the tree structure. The methodology for building our decision tree for KW-4 igniters was presented. It involves determining features of tested igniters and applied predictors, which are necessary to create the correct model of the tree. The classification tree was built based on the possessed test results, determining the adopted post-diagnostic decision as the qualitative independent variable. The schema of the resultant classification tree and the full structure of this tree toget

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Liu, Yuheng, Chenxuan Zhang, Xiaoyang Zheng, Yuhan Liu, and Jiangping He. "Stroke Prediction Model Based on Decision Tree." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 20 (March 7, 2023): 24–27. http://dx.doi.org/10.37394/23208.2023.20.3.

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In this paper, the predictive model of stroke based on decision tree is implemented to predict the stroke probability of ten samples by using Python language. The dataset of stroke is collected and is preprocessed, then the Gini coefficients of each feature are calculated to select the division, and then the decision tree model is obtained. Finally, the stroke probability is predicted for ten samples. In addition, Naive Bayes model is applied to predict the stroke probability to compare with the decision tree method. The experimental results show that older people with high blood pressure, hea

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Jiang, Yuze. "Personalized Thermal Comfort Model with Decision Tree." Intelligent Control and Automation 10, no. 04 (2019): 168–77. http://dx.doi.org/10.4236/ica.2019.104012.

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Syed Nor, Sharifah Heryati, Shafinar Ismail, and Bee Wah Yap. "Personal bankruptcy prediction using decision tree model." Journal of Economics, Finance and Administrative Science 24, no. 47 (2019): 157–70. http://dx.doi.org/10.1108/jefas-08-2018-0076.

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Purpose Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties

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Lovász, László, Moni Naor, Ilan Newman, and Avi Wigderson. "Search Problems in the Decision Tree Model." SIAM Journal on Discrete Mathematics 8, no. 1 (1995): 119–32. http://dx.doi.org/10.1137/s0895480192233867.

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Safrizal. "Somnambulism Classification Model Using Decision Tree Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 1875–82. https://doi.org/10.59934/jaiea.v4i3.1040.

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Somnambulism, commonly known as sleepwalking, is a sleep disorder classified under parasomnias and poses potential dangers to both the individual affected and those nearby. This condition often goes unnoticed by the person experiencing it, making early detection and intervention challenging. This study aims to develop a classification model for somnambulism using the C4.5 decision tree algorithm, focusing on identifying key risk factors and supporting early diagnosis and treatment strategies. The research adopts the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, which

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Budiana, Stevanny, Felivia Kusnadi, and Robyn Irawan. "BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (2023): 0135–46. http://dx.doi.org/10.30598/barekengvol17iss1pp0135-0146.

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Bayesian Additive Regression Tree (BART) is a sum-of-trees model used to approximate classification or regression cases. The main idea of this method is to use a prior distribution to keep the tree size small and a likelihood from data to get the posterior. By fixing the tree size as small as possible, the approximation of each tree would have a little effect on the posterior, which is the sum of all output from all the trees used. Bayesian additive regression tree method will be used for predicting the maternity recovery rate of group long-term disability insurance data from the Society of Ac

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Cheng, Kuo-Chih, Mu-Jung Huang, Cheng-Kai Fu, Kuo-Hua Wang, Huo-Ming Wang, and Lan-Hui Lin. "Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques." Sustainability 13, no. 6 (2021): 3100. http://dx.doi.org/10.3390/su13063100.

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This study attempts to integrate the decision tree algorithm with the Apriori algorithm to explore the relationship among financial ratio, corporate governance, and stock returns to establish a stock investment decision model. The sports and leisure related industries are employed as the research target. The data are collected and processed for generating decision tree and association rules. Based on the analysis outcome, an investment decision model is constructed for investors expecting to decrease their investment risks and further increase their profits. This stock investment decision mode

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Quan, Zhiyu, and Emiliano A. Valdez. "Predictive analytics of insurance claims using multivariate decision trees." Dependence Modeling 6, no. 1 (2018): 377–407. http://dx.doi.org/10.1515/demo-2018-0022.

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AbstractBecause of its many advantages, the use of decision trees has become an increasingly popular alternative predictive tool for building classification and regression models. Its origins date back for about five decades where the algorithm can be broadly described by repeatedly partitioning the regions of the explanatory variables and thereby creating a tree-based model for predicting the response. Innovations to the original methods, such as random forests and gradient boosting, have further improved the capabilities of using decision trees as a predictive model. In addition, the extensi

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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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Syahra, Yohanni, Yuni Franciska Br. Tarigan, Karina Andriani, Hevlie Winda Nazry S, and Roziyani Setik. "Decision Trees in Predicting Loan Default Risk in Customer Relationships within the Financial Sector." Sinkron 9, no. 2 (2025): 734–45. https://doi.org/10.33395/sinkron.v9i2.14672.

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Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profil

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Tamilmani.G and Rajathi.K. "Customer Retention in Banking Sector using Decision Tree-Neuro Based System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 481–85. https://doi.org/10.35940/ijeat.C4856.029320.

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All the bank marketing campaigns mostly deals with large amount of data. when they need to deal with huge electronic data of customers, then it is very difficult to analyze the data manually or by human analyst. Here comes the picture of data mining techniques to deal with the large amount of data and to come up with useful data which helps in decision making process. All the data mining techniques helps in analyzing the data. some of the techniques that can be used for this bank marketing campaigns are naive bayes, logistics regression technique and Decision tree model technique etc. among al

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Solway, Alec, and Matthew M. Botvinick. "Evidence integration in model-based tree search." Proceedings of the National Academy of Sciences 112, no. 37 (2015): 11708–13. http://dx.doi.org/10.1073/pnas.1505483112.

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Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivi

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Saifullah, Saifullah, Muhammad Zarlis, Zakaria Zakaria, and Rahmat Widia Sembiring. "Analisa Terhadap Perbandingan Algoritma Decision Tree Dengan Algoritma Random Tree Untuk Pre-Processing Data." J-SAKTI (Jurnal Sains Komputer dan Informatika) 1, no. 2 (2017): 180. http://dx.doi.org/10.30645/j-sakti.v1i2.41.

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Preprocessing data is needed some methods to get better results. This research is intended to process employee dataset as preprocessing input. Furthermore, model decision algorithm is used, random tree and random forest. Decision trees are used to create a model of the rule selected in the decision process. With the results of the preprocessing approach and the model rules obtained, can be a reference for decision makers to decide which variables should be considered to support employee performance improvement

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Fang, Shenghao. "Research on Chinese Public Policy Decision Model Based on Decision Tree Algorithm." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 301–7. http://dx.doi.org/10.54097/hset.v56i.10592.

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In this paper, the decision tree classification algorithm is used to establish the prediction model of Chinese public policy decision. This paper selects characteristic attributes based on the principle of policy making. Then, taking the result of public policy decision as the target label, this paper optimizes the model by adjusting the maximum depth of decision tree, the minimum number of leaf samples and the decision threshold. The test set verifies that the optimized decision tree model has a good predictive effect on the result prediction of the public policy decision model. The value of

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Alajali, Walaa, Wei Zhou, Sheng Wen, and Yu Wang. "Intersection Traffic Prediction Using Decision Tree Models." Symmetry 10, no. 9 (2018): 386. http://dx.doi.org/10.3390/sym10090386.

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Traffic prediction is a critical task for intelligent transportation systems (ITS). Prediction at intersections is challenging as it involves various participants, such as vehicles, cyclists, and pedestrians. In this paper, we propose a novel approach for the accurate intersection traffic prediction by introducing extra data sources other than road traffic volume data into the prediction model. In particular, we take advantage of the data collected from the reports of road accidents and roadworks happening near the intersections. In addition, we investigate two types of learning schemes, namel

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Wang, Zijun, and Keke Gai. "Decision Tree-Based Federated Learning: A Survey." Blockchains 2, no. 1 (2024): 40–60. http://dx.doi.org/10.3390/blockchains2010003.

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Federated learning (FL) has garnered significant attention as a novel machine learning technique that enables collaborative training among multiple parties without exposing raw local data. In comparison to traditional neural networks or linear models, decision tree models offer higher simplicity and interpretability. The integration of FL technology with decision tree models holds immense potential for performance enhancement and privacy improvement. One current challenge is to identify methods for training and prediction of decision tree models in the FL environment. This survey addresses thi

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Gunduz, Murat, and Hamza M. A. Lutfi. "Go/No-Go Decision Model for Owners Using Exhaustive CHAID and QUEST Decision Tree Algorithms." Sustainability 13, no. 2 (2021): 815. http://dx.doi.org/10.3390/su13020815.

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Go/no-go execution decisions are one of the most important strategic decisions for owners during the early stages of construction projects. Restructuring the process of decision-making during these early stages may have sustainable results in the long run. The purpose of this paper is to establish proper go/no-go decision-tree models for owners. The decision-tree models were developed using Exhaustive Chi-square Automatic Interaction Detector (Exhaustive CHAID) and Quick, Unbiased, Efficient Statistical Tree (QUEST) algorithms. Twenty-three go/no-go key factors were collected through an extens

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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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AMPUŁA (ampulad@witu.mil.pl), Dariusz. "Using Interactive Decision Tree Models in Artillery Fuse Testing." Problems of Mechatronics Armament Aviation Safety Engineering 15, no. 1 (2024): 23–38. http://dx.doi.org/10.5604/01.3001.0054.4486.

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In the introduction, the concept of interactive trees is defined and the purpose of the study is presented. Then, the RGM-2 fuse is described, as are the results of its tests which served as a basis for building specific models. The types of ammunition in which this variation of an artillery fuse is used are listed. A method of building interactive classification trees, allowing the author of the model to interfere with its structure, is described as well. Models of interactive classification trees, such as C&RT, CHAID and XAID have been designed and built. For each model, a tree diagram,

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Syafitri, Nesi, Syarifah Farradinna, Wella Jayanti, and Yudhi Arta. "MACHINE LEARNING TO CREATE DECISION TREE MODEL TO PREDICT OUTCOME OF ENTERPRENEURSHIP PSYCHOLOGICAL READINESS (EPR)." Jurnal Teknik Informatika (Jutif) 4, no. 2 (2023): 381–90. http://dx.doi.org/10.52436/1.jutif.2023.4.2.590.

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This study aims to create a decision tree model using machine learning to predict psychological readiness for entrepreneurship in college graduates. This research was conducted through several stages of research. In the early stages, a survey was conducted on 700 students from several universities in Riau aged between 17-25 years. The survey was conducted using the Entrepreneur Psychology Readiness (EPR) instrument. Furthermore, the survey data was validated and obtained 604 valid data to be used in forming machine learning models The urgency of this research is to find a number of decision ru

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Heshmatol Vaezin, S. M., J. L. Peyron, and F. Lecocq. "A simple generalization of the Faustmann formula to tree level." Canadian Journal of Forest Research 39, no. 4 (2009): 699–711. http://dx.doi.org/10.1139/x08-202.

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The economic decision model serving as an objective function in forest economics was conceived originally by Faustmann at the stand level. Nevertheless, the tree level seems to be an appropriate scale for analysis, especially for harvesting decisions and certain estimations both at tree and stand levels. However, the Faustmann formula cannot be directly applied to the tree level. The present research has provided certain tree-level formulations of the Faustmann formula, including, in particular, tree expectation value (TEV) and land expectation value (LEV). TEV and tree-level LEV formulas were

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McTavish, Hayden, Chudi Zhong, Reto Achermann, et al. "Fast Sparse Decision Tree Optimization via Reference Ensembles." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 9604–13. http://dx.doi.org/10.1609/aaai.v36i9.21194.

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Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, part

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