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.

Autorentext
Sihem, Benkhaled
Benkhaled Sihem is a computer scientist, graduated from university of Abbes Laghrour - Khenchela, Algeria. Has a master degree in Software Engineering and Distributed Systems specialty (2017).She prepared a Ph.D thesis in the same specialty.