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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Sinha, Sitesh Kumar
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Computer Science & IT Engineering

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Feature selection techniques for microarray dataset: a review Nagaraja, Avinash; Sinha, Sitesh Kumar; Mallaiah, Shivamurthaiah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2395-2402

Abstract

Automatic speech recognition (ASR) approach is dependent on optimal for many researchers working on feature selection (FS) techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create FS approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary FS techniques, such as filter, wrapper, and embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on critical review questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of FS strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment.
Binary white shark optimization algorithm with Z-shaped transfer function for feature selection problems Rao, Avinash Nagaraja; Sinha, Sitesh Kumar; Mallaiah, Shivamurthaiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1269-1279

Abstract

Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.
Co-Authors Mallaiah, Shivamurthaiah Nagaraja, Avinash Rao, Avinash Nagaraja