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In today world of enormous amounts of data, it is very important to extractuseful knowledge from it. This can be accomplished by feature subset selection.Feature subset selection is a method of selecting a minimum number of featureswith the help of which our machine can learn and predict which class aparticular data belongs to. We will introduce a new adaptive algorithm calledFeature selection Penguin Search optimization algorithm which is ametaheuristic approach. It is adapted from the natural hunting strategy ofpenguins in which a group of penguins take jumps at random depths and come backand 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 subsetcandidates, the bioinspired approach Penguin Search optimization algorithmgenerates during the process a trial feature subset and estimates its fitnessvalue by using three different classifiers for each case: Random Forest,Nearest Neighbour and Support Vector Machines. However, we are planning toimplement our proposed approach Feature selection Penguin Search optimizationalgorithm on some well known benchmark datasets collected from the UCIrepository and also try to evaluate and compare its classification accuracywith some state of art algorithms.

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