An evolutionary decomposition-based multi-objective feature selection for multi-label classification.

ABSTRACT: Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each entity has more than one label. To improve the performance of multi-label classification, feature selection plays an important role. It involves identifying and removing irrelevant and redundant features that unnecessarily increase the dimensions of the search space for the classification problems. However, classification may fail with an extreme decrease in the number of relevant features. Thus, minimizing the number of features and maximizing t

SUBMITTER: Asilian Bidgoli A 

PROVIDER: S-EPMC7924502 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature