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

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 nine attributes and one class (label). The suggested model was evaluated with two existing classification models (C4.5 and Naïve 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) Application of forward selection strategy e15