A quantum particle swarm optimization approach for feature selection

This book represent a feature selection approach. Feature selection is a pre-processing step that plays an important role in data mining allowing it to search a reduced subset of features from a large set of features by eliminating redundant and irrelevant features for performing the supervised classification task all trying to maintain or improve classifier performance. The search for a subset of features is an NP-difficult optimization problem that can be solved by metaheuristics; we have been interested in the metaheuristics resulting from the intelligence in swarms for the features selection.