Incremental feature selection for hierarchical classification based on fuzzy rough sets
The number of class labels has increased dramatically in the age of big data,posing a serious challenge to exist-ing classification tasks.To solve this problem,people typically organize the labels into a hierarchical structure and then use the information in the structure to learn the task.Considering the increasing number of samples,an incremental feature se-lection algorithm for hierarchical classification is designed by using fuzzy rough sets-based information entropy.Firstly,con-sidering the sibling strategy,the existing λ conditional entropy is generalized to the case of hierarchical classification,and a non-incremental hierarchical classification feature selection algorithm is designed.Secondly,the λ incremental conditional entropy is defined,based on which an incremental version of the feature selection algorithm is designed.In the experiment,seven different feature selection algorithms,including the non-incremental version,are used to compare with the incremen-tal algorithm on five hierarchical datasets.The experimental results verify the effectiveness of the two algorithms,and the design incremental algorithm can accelerate the process of feature selection without affecting performance.
万方数据
维普
