  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.
