Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM

Imam Cholissodin; Nanik Suciati; Darlis Herumurti; Riyanarto Sarno; Valentina Yurina

doi:10.1109/ICTS58770.2023.10330844

Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM

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Imam Cholissodin, Nanik Suciati, Darlis Herumurti, Riyanarto Sarno, Valentina Yurina

2023 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023 Conference paper Cited by 1

Abstract

In the bio-molecular field, epitope classification is essential in vaccine development. A machine learning-based approach has been used for epitope classification using peptide data from b-cell, Severe Acute Respiratory Syndrome (SARS) to predict material for SARS-CoV-2 vaccine. This study aims to improve the performance of epitope classification by weighting peptide features and including input weight and biases of Classifier model using the Time Variant Inertia Weight, Acceleration Coefficients and Random Injection-Particle Swarm optimization (TVIWACRI-PSO) and the Extreme Learning Machine (ELM) classification algorithm. Experiment on b-cell, SARS dataset shows that feature weighting (FW) using TVIWACRI-PSO can improve accuracy performance by 8.4%. © 2023 IEEE.

Affiliations

Universitas Brawijaya, Department of Informatics Engineering, Malang, Indonesia; Institut Teknologi Sepuluh Nopember, Department of Informatics, Surabaya, Indonesia; Universitas Brawijaya, Department of Pharmacy, Malang, Indonesia

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