A Multi-Objective Particle Swarm Optimization For Feature Selection

A Multi-Objective Particle Swarm Optimization For Feature Selection

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Feature selection is very important task in classification. Number of features is available for classification but not all of them are useful. Irrelevant and redundant features may even reduce the performance. There are two types of feature selection approaches. They are wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm when searching the goodness of the features during the feature selection process while filters are independent of any classification algorithm. The goal of Feature selection is to choose a small number of relevant features to a...