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All Journal International Journal of Electrical and Computer Engineering
Sepiribo Lucky Braide
University of Port Harcourt
Computer Science & IT Electrical & Electronics Engineering

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Multivariate sample similarity measure for feature selection with a resemblance model Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Crescent Onyebuchi Omeje; Sepiribo Lucky Braide
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3359-3366

Abstract

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
Co-Authors Ayodeji Olalekan Salau Crescent Onyebuchi Omeje Tsehay Admassu Assegie