A comprehensive study on pre-pruning and post-pruning methods of decision tree classification algorithm

Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Billah, Md. Masum; Das, Protiva; Muna, Jannatun Naeem; Ranjan, Rumesh

doi:10.1109/icoei51242.2021.9452898

Cited by 18 publications

(4 citation statements)

References 19 publications

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“…The naïve Bayes algorithm predicts data using Bayes' Theorem calculations. Bayes' theorem is a statistical approach [15]. Bayes' Theorem calculations are carried out by calculating the probability of a hypothesis based on new evidence and data.…”

Section: Naïve Bayesmentioning

confidence: 99%

Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees

Sartika,

Gustriansyah

2024

tekno

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The employee performance assessment at the Palembang City Fire and Disaster Management Service (DPKPB) is applied to other than the employee performance assessment implementation team based on the Decree of the Head of the Palembang City DPKPB Number 146 of 2021 concerning the employee performance assessment implementation team and awards for exemplary employees. Subjective assessments are avoided to obtain assessment results that are by the achievements of each employee. The application of data mining can be an alternative to avoid subjectivity in performance assessment. In this research, a comparison of the Naive Bayes and Decision Tree algorithms was carried out to assess the performance of Palembang City DPMPB employees. The results of further research will be used as an alternative solution in conducting performance assessments that are more objective than previous assessments. Both algorithms were evaluated for model performance using the Confusion Matrix. Based on the results of the evaluation carried out, it was stated that the Decision Tree algorithm had better accuracy, namely 91.74% compared to Naïve Bayes which had an accuracy of 88.99% with a test size of 0.4

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Section: Naïve Bayesmentioning

confidence: 99%

Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees

Sartika,

Gustriansyah

2024

tekno

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“…To overcome this problem in ML models, it is necessary to prune and remove some nodes or branches. 26…”

Section: Dtmentioning

confidence: 99%

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Xu¹,

Zhang²,

Hu³

et al. 2023

DMSO

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The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population. Patients and Methods:The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). Results: Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%. Conclusion:This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.

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“…Decision trees are one of the most popular machine learning algorithms, dividing data repeatedly to form classes or groups [1]. Decision tree as a classification method is very effective [2], where classification tasks are modeling with a set of hierarchical decisions on feature variables in the form of a tree [3]. Classification algorithms in the decision tree include ID3, C4.5, and CART [4].…”

Section: Introductionmentioning

confidence: 99%

“…Overfitting makes the classifier decrease in accuracy due to failure to properly generalize unseen instances [7]. For this, it is necessary to pruning [2]. Pruning is the process of cutting or removing unwanted nodes and branches, overfitting the decision tree [8].…”

Section: Introductionmentioning

confidence: 99%

Improved Decision Tree Accuracy (C4.5) with Attribute Reduction Using Forward Selection in Data Classification

Yohannes Cristanto,

Zamzami,

Fahmi

2024

Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis,

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The main process in the formation of Decision Tree C4.5 is the separation of attributes. However, the attribute separation procedure in C4.5 still cannot optimize prediction accuracy in decision tree formation because unwanted features can lead to noisy data and less relevant features, which in turn can result in very large decision tree sizes (overfitting). As a result, the data becomes unbalanced and the classification accuracy of the Decision Tree C4.5 model becomes lower. To improve the accuracy of the classification process, attribute reduction is performed as a technique to simplify less relevant attributes. Therefore, forward selection is proposed as an attribute reduction method to produce mutually uncorrelated features, which are then used in Decision Tree C4.5 for classification. This study used datasets from the UCI Machine Learning Repository and Kaggle.com namely Diabetic Retinopathy Debrecen and South German Credit. Debrecen's Diabetic Retinopathy consists of 1,151 data records with 20 attributes, while South German Credit consists of 1000 data records with 20 attributes. Evaluation of classification performance is carried out based on the calculation of the Confusion Matrix. The test results showed that the proposed method was able to increase classification accuracy by 7.68%. Therefore, forward selection is considered an effective technique in reducing attributes and improving classification accuracy in Decision Tree C4.

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A comprehensive study on pre-pruning and post-pruning methods of decision tree classification algorithm

Cited by 18 publications

References 19 publications

Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees

Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Improved Decision Tree Accuracy (C4.5) with Attribute Reduction Using Forward Selection in Data Classification

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