A Study and Analysis of a Feature Subset Selection Technique using Penguin Search Optimization Algorithm (FS-PeSOA)
This addresses the problem of handling large datasets for machine learning practitioners, but appears incremental as it adapts an existing optimization approach to feature selection.
The paper tackles feature subset selection by proposing a new adaptive metaheuristic algorithm, FS-PeSOA, inspired by penguin hunting behavior, and plans to evaluate its classification accuracy on benchmark datasets compared to state-of-the-art methods.
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.