Adaptive feature selection using v-shaped binary particle swarm optimization.

ABSTRACT: Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information

SUBMITTER: Teng X 

PROVIDER: S-EPMC5373580 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature