Application of the forward selection strategy to the C4.5 Algorithm to improve the classification's accuracy of a breast cancer data set

Main Article Content

Robbi Rahim

Keywords

Forward Selection, Data Mining, Classification, Method C4.5, Breast Cancer

Abstract

The purpose of this study is to improve the classification accuracy of the C4.5 Algorithm utilizing the forward selection technique. Breast Cancer from the UCI Machine Learning Repository is the dataset utilized. There are 286 records in the dataset with 9 attributes and 1 class (label). The suggested model was evaluated with two existing classification models (C4.5 and Naive Bayes) using the RapidMiner program. The procedure consists of multiple stages, the first of which consists of selecting the dominant trait using the feature selection technique (weight by information gain). The second step is forward selection based on the outcome of feature selection. Before processing, the dataset is separated into training and testing halves. Where the ratios of comparison are 70:30, 80:20, and 90:10 The final step is examining the output. The experimental results demonstrate that the forward selection methodology employing the C4.5 (C4.5+FS) method outperforms the C4.5 and Nave Bayes classification techniques. C4.5+FS (Split Data 70:30) has an accuracy value of 76.74 percent, C4.5+FS (Split Data 80:20) has an accuracy value of 78.95 percent, C4.5+FS (Split Data 90:10) has an accuracy value of 78.57 percent, C4.5 (Split Data 70:30) has an accuracy value of 65.12 percent, and Nave Bayes (Split Data is 70:30) has an accuracy value In comparison to typical classification algorithms (C4.5 and Naive Bayes), the average accuracy values increased by 12.97 percent and 8.32 percent, respectively. In terms of precision, recal, and f-measure, the forward selection strategy utilizing the C4.5 method beat all other classification techniques, achieving 79.84 percent, 92.50 percent, and 85.55 percent, respectively. In addition, the results demonstrated an increase in the average AUC from 0.628% to 0.732%. Therefore, it can be inferred that the forward selection strategy can be applied to the Breast Cancer Data Set in order to increase the accuracy value of classification method C4.5.

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