Feature Selection Using BMNABC

This folder contains an implementation for the feature selection problem using Binary Multi-Neighborhood Artificial Bee Colony (BMNABC).

The feature selection (feature subset selection) problem is one of the important pre-processing phases in various areas . In real datasets, many irrelevant, misleading and redundant features exist that are useless. Main features can be extracted by feature selection technique. The feature selection is in the class of NP-hard problems; therefore, meta-heuristic algorithms are useful to solve the problem. A new binary ABC called binary multi-neighborhood artificial bee colony (BMNABC) has been introduced to enhance the exploration and exploitation abilities in the phases of ABC. BMNABC applies the near and far neighborhood information with a new probability function in the first and second phases. A more conscious search than the standard ABC is done in the third phase for those solutions which have been not improved in the previous phases. The algorithm can be combined with the wrapper approach to achieve the best results.