Multi-Objective Bees Algorithm for Feature Selection

Natalia Hartono, Natalia Hartono

2021

Abstract

In machine learning, there are enormous features that can affect learning performance. The problem is that not all the features are relevant or important. Feature selection is a vital first step in finding a smaller number of relevant features. The feature selection problem is categorised as an NP-hard problem, where the possible solution exponentially surges when the number of n-dimensional features increases. Previous research in feature selection has shifted from single-objective to multi-objective because there are two conflicting objectives: minimising the number of features and minimising classification errors. Bees Algorithm (BA) is one of the most popular metaheuristics for solving complex problems. However, none of the previous studies used BA in feature selection using a multi-objective approach. This paper aims to present the first study using the Multi-objective Bees Algorithm (MOBA) as a wrapper approach in feature selection. The MOBA developed for this study using basic combinatorial BA with combinatorial of swap, insertion and reversion as local operators with Non-Dominated Sorting and Crowding Distance to find the Pareto Optimal Solutions. The performance evaluation using nine Machine Learning classifiers shows that MOBA performs well in classification. Future work will improve the MOBA and use larger datasets.

Paper Citation

in Harvard Style

Hartono N. (2021). Multi-Objective Bees Algorithm for Feature Selection. In Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science - Volume 1: ICE-TES, ISBN 978-989-758-601-9, pages 358-369. DOI: 10.5220/0010754200003113

in Bibtex Style

@conference{ice-tes21,
author={Natalia Hartono},
title={Multi-Objective Bees Algorithm for Feature Selection},
booktitle={Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science - Volume 1: ICE-TES,},
year={2021},
pages={358-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010754200003113},
isbn={978-989-758-601-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science - Volume 1: ICE-TES,
TI - Multi-Objective Bees Algorithm for Feature Selection
SN - 978-989-758-601-9
AU - Hartono N.
PY - 2021
SP - 358
EP - 369
DO - 10.5220/0010754200003113