Performance Evaluation Among ID3, C4.5, and CART Decision Tree Algorithm

Shamrat, F. M. Javed Mehedi; Ranjan, Rumesh; Hasib, Khan Md.; Yadav, Amit; Siddique, Abdul Hasib

doi:10.1007/978-981-16-5640-8_11

Cited by 30 publications

(15 citation statements)

References 19 publications

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“…To further support the results listed above, 5-fold cross-validation was utilized to test various more advanced classifiers. Simultaneously, we compared the XGBoost classifier with the other three commonly used classifiers, including classification and regression tree (CART classifier), 21 , 22 Gaussian naive Bayes (GNB classifier), 23 and support vector machine (SVM). All these classifiers are realized on the standard dataset and the same features by using 5-fold cross-validation.…”

Section: Resultsmentioning

confidence: 99%

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions

Chen¹,

Zhao

et al. 2023

Molecular Therapy - Nucleic Acids

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Section: Resultsmentioning

confidence: 99%

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions

Chen¹,

Zhao

et al. 2023

Molecular Therapy - Nucleic Acids

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“…For both classification and regression issues, decision trees (DTs) are a go-to standard supervised machine learning model [39]. It is a model like a tree, with nodes and branches for selecting the predictor variable that will lead to the most consistent possible subsets of data concerning the target variable [40,41]. DTs are well-known for their ability to classify data with minimal effort and high dependability.…”

Section: Decision Treementioning

confidence: 99%

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Nobel,

Sultana,

Singha

et al. 2024

Information

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Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry.

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“…This approach/technique was chosen because it is naturally explainable and understood, in a certain way to imitate human thought. There are some well-known decision tree implementation variants by the scientific community, to highlight the Iterative Dichotomiser 3 (ID3), C4.5, C5.0 and Classification and Regression Trees (CART) algorithms [11].…”

Section: Decision Tree Apimentioning

confidence: 99%

Explainable Decision Tree on Smart Human Mobility

Rosa

Guimarães

Carneiro

et al. 2022

Ambient Intelligence and Smart Environments

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Artificial Intelligence is a hot topic and Machine Learning is one of the most fluent approaches and practices. The problem with many AI models is that they can be useful for predicting but they are bad at explaining why they behave a certain way. In some contexts, the explanation may even be more important than the prediction itself, mainly in systems in which decisions are made based on their predictions. Therefore, it is increasingly necessary to provide a forecast accompanied by an explanation, when decisions are made automatically. This paper aims to contribute to the solution of problem based on human mobility research, or at least, to be a starting point for its solution.

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Performance Evaluation Among ID3, C4.5, and CART Decision Tree Algorithm

Cited by 30 publications

References 19 publications

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Explainable Decision Tree on Smart Human Mobility

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