Binary Representation of Polar Bear Algorithm for Feature Selection

In most of the scientific research feature selection is a challenge for researcher. Selecting all available features is not an option as it usually complicates the research and leads to performance drop when dealing with large datasets. On the other hand, ignoring some features can compromise the data accuracy. Here the rough set theory presents a good technique to identify the redundant features which can be dismissed without losing any valuable information, however, exploring all possible combinations of features will end with NP-hard problem. In this research we propose adopting a heuristic algorithm to solve this problem, Polar Bear Optimization PBO is a metaheuristic algorithm provides an effective technique for solving such kind of optimization problems. Among other heuristic algorithms it proposes a dynamic mechanism for birth and death which allows keep investing in promising solutions and keep dismissing hopeless ones. To evaluate its efficiency, we applied our proposed model on several datasets and measured the quality of the obtained minimal feature set to prove that redundant data was removed without data loss.