@misc{10481/70257,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/70257},
abstract = {Classification tasks often include, among the large number of features to be processed in
the datasets, many irrelevant and redundant ones, which can even decrease the ef ciency of classi ers.
Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks
of the high dimensionality of datasets and often has two con icting objectives: The  rst function aims to
maximize the classi cation performance or reduce the error rate of the classi er. In contrast, the second
function is designed to minimize the number of features. However, the majority of wrapper FS techniques
are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the
well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of
MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from
local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the
memory concept and personal best of the universes. The experimental results and comparisons indicate that
the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and
maintain a minimum classi cation error rate when dealing with different datasets compared with the most
popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed
approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.},
organization = {Spanish Government	TIN2017-85727-C4-2P},
publisher = {IEEE},
keywords = {Wrapper feature selection},
keywords = {Multi-verse algorithm},
keywords = {Optimization},
keywords = {Classification},
title = {A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization},
doi = {10.1109/ACCESS.2021.3097206},
author = {Aljarah, Ibrahim and Al-Zoubi, Ala´ M. and Castillo Valdivieso, Pedro Ángel and Merelo Guervos, Juan Julián},
}