A review of popular decision tree algorithms in data mining

Rana, A. M.; Pandey, Rajesh

doi:10.5958/2278-4853.2021.00837.5

Cited by 9 publications

(6 citation statements)

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“…The decision tree (DT) is a tree structure (can be binary or non-binary). DT includes the ID3 algorithm [39] , C4.5 algorithm [40] , and CART algorithm [41] , which are the core technologies for classification and prediction [42] . The ID3 algorithm mainly aims to construct a DT recursively and select features at each node of the DT by applying the information gain criterion.…”

Section: Decision Treementioning

confidence: 99%

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang

et al. 2023

Big Data Min. Anal.

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The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R 2 / as the evaluation indicator (R 2 : 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R 2 : 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment.

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Section: Decision Treementioning

confidence: 99%

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang

et al. 2023

Big Data Min. Anal.

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“…In the process of training the decision tree, the appropriate node partition attribute according to the specific problem can be selected, and the information gain or the size of Gini coefficient are compared, selecting the attribute that makes the greatest improvement in purity as the partition attribute. By recursively dividing sub nodes, a decision tree is constructed to classify new samples [11][12].…”

Section: Decision Treementioning

confidence: 99%

Network accounting information security based on classification and regression tree algorithm

li,

Tham

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…Dk and tested on D2 and so on. The C4.5 algorithm is a modification of the ID3 algorithm that employs information entropy, continuous and discrete characteristics, categorical and numeric attributes, and missing values [29]. The phase of testing the C4.5 algorithm is carried out using the following steps:…”

Section: E Evaluation Of the J48 Decision Tree Classifier Using K-fol...mentioning

confidence: 99%

Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

Muhasshanah¹,

Tohir²,

Ningsih³

et al. 2023

CommIT (Communication and Information Technology) Journal

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Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.

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A review of popular decision tree algorithms in data mining

Cited by 9 publications

References 0 publications

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Network accounting information security based on classification and regression tree algorithm

Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

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