A Study and Analysis of a Feature Subset Selection Technique using Penguin Search Optimization Algorithm (FS-PeSOA)

TL;DR

This paper introduces FS-PeSOA, a new bioinspired feature selection algorithm based on penguin hunting strategies, aiming to improve classification accuracy on benchmark datasets using multiple classifiers.

Contribution

The paper proposes a novel adaptive metaheuristic algorithm, FS-PeSOA, inspired by penguin hunting behavior, for effective feature subset selection in machine learning.

Findings

Demonstrates the effectiveness of FS-PeSOA on UCI datasets.

Compares classification accuracy with existing algorithms.

Uses multiple classifiers to evaluate feature subsets.

Abstract

In today world of enormous amounts of data, it is very important to extract useful knowledge from it. This can be accomplished by feature subset selection. Feature subset selection is a method of selecting a minimum number of features with the help of which our machine can learn and predict which class a particular data belongs to. We will introduce a new adaptive algorithm called Feature selection Penguin Search optimization algorithm which is a metaheuristic approach. It is adapted from the natural hunting strategy of penguins in which a group of penguins take jumps at random depths and come back and share the status of food availability with other penguins and in this way, the global optimum solution is found. In order to explore the feature subset candidates, the bioinspired approach Penguin Search optimization algorithm generates during the process a trial feature subset and estimates its fitness value by using three different classifiers for each case: Random Forest, Nearest Neighbour and Support Vector Machines. However, we are planning to implement our proposed approach Feature selection Penguin Search optimization algorithm on some well known benchmark datasets collected from the UCI repository and also try to evaluate and compare its classification accuracy with some state of art algorithms.