Minimizing features while maintaining performance in data classification problems

Abstract

High dimensional classification problems have gained increasing attention in machine learning, and feature selection has become essential in executing machine learning algorithms. In general, most feature selection methods compare the scores of several feature subsets and select the one that gives the maximum score. There may be other selections of a lower number of features with a lower score, yet the difference is negligible. This article proposes and applies an extended version of such feature selection methods, which selects a smaller feature subset with similar performance to the original subset under a pre-defined threshold. It further validates the suggested extended version of the Principal Component Loading Feature Selection (PCLFS-ext) results by simulating data for several practical scenarios with different numbers of features and different imbalance rates on several classification methods. Our simulated results show that the proposed method outperforms the original PCLFS and existing Recursive Feature Elimination (RFE) by giving reasonable feature reduction on various data sets, which is important in some applications.

Early detection of student degree-level academic performance using educational data mining

“…Although the amount of data used to train a classifier has great influence on the effectiveness of the generated model, the size of the data alone does not ensure the accuracy and quality of the generated model ( Asif et al, 2017 ). The number of attributes (dimensions / features) being explored, the level of influence these attributes have on the prediction of the class label, and the removal of attributes that inversely affect the prediction of the class label can greatly improve the quality of the generated model ( Matharaarachchi, Domaratzki & Muthukumarana, 2022 ). Thus, an important step before knowledge discovery is ensuring the use of optimal attributes for the classifier ( Farsi, 2021 ).…”