Fire Fly Based Feature Selection Approach

Irrelevant, noisy and high dimensional data, containing large number of features, degrades the performance of data mining and machine learning tasks. One of the methods used in data mining to reduce the dimensionality of data is feature selection. Feature selection methods select a subset of features that represents original features in problem domain with high accuracy. Various methods have been proposed that utilize heuristic or nature inspired strategies along with Rough Set Theory (RST) to find these subsets. However these methods either consume more time to find subset or compromise with optimality. The paper presents a new feature selection approach that combines the RST with nature inspired ‘firefly’ algorithm. The algorithm simulates the attraction system of real fireflies that guides the feature selection procedure. The experimental result proves that the proposed algorithm scores over other feature selection method in terms of time and optimality.