Multi-objective Classification and Feature Selection of Covid-19 Proteins Sequences using NSGA-II and MAP-Elites

Vijay Sambhe, Shanmukha Rajesh, Enrique Naredo, Enrique Naredo, Douglas Dias, Douglas Dias, Douglas Dias, Meghana Kshirsagar, Meghana Kshirsagar, Conor Ryan, Conor Ryan

Abstract

The advent of the Covid-19 pandemic has resulted in a global crisis making the health systems vulnerable, challenging the research community to find novel approaches to facilitate early detection of infections. This open-up a window of opportunity to exploit machine learning and artificial intelligence techniques to address some of the issues related to this disease. In this work, we address the classification of ten SARS-CoV-2 protein sequences related to Covid-19 using k-mer frequency as features and considering two objectives; classification performance and feature selection. The first set of experiments considered the objectives one at the time, four techniques were used for the feature selection and twelve well known machine learning methods, where three are neural network based for the classification. The second set of experiments considered a multi-objective approach where we tested a well known multi-objective approach Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites), which considers quality+diversity containers to guide the search through elite solutions. The experimental results shows that ResNet and PCA is the best combination using single objectives. Whereas, for the mulit-classification, NSGA-II outperforms ME with two out of three classifiers, while ME gets competitive results bringing more diverse set of solutions.

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Paper Citation


in Harvard Style

Sambhe V., Rajesh S., Naredo E., Dias D., Kshirsagar M. and Ryan C. (2021). Multi-objective Classification and Feature Selection of Covid-19 Proteins Sequences using NSGA-II and MAP-Elites.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1241-1248. DOI: 10.5220/0010388512411248


in Bibtex Style

@conference{icaart21,
author={Vijay Sambhe and Shanmukha Rajesh and Enrique Naredo and Douglas Dias and Meghana Kshirsagar and Conor Ryan},
title={Multi-objective Classification and Feature Selection of Covid-19 Proteins Sequences using NSGA-II and MAP-Elites},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1241-1248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010388512411248},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Multi-objective Classification and Feature Selection of Covid-19 Proteins Sequences using NSGA-II and MAP-Elites
SN - 978-989-758-484-8
AU - Sambhe V.
AU - Rajesh S.
AU - Naredo E.
AU - Dias D.
AU - Kshirsagar M.
AU - Ryan C.
PY - 2021
SP - 1241
EP - 1248
DO - 10.5220/0010388512411248