Release Time:2019-03-13  Hits:

Indexed by:Journal Article

Date of Publication:2016-04-01

Journal:JOURNAL OF BIOMEDICAL INFORMATICS

Included Journals:PubMed、EI、SCIE

Volume:60

Page Number:234-242

ISSN:1532-0464

Key Words:Classification; Ranking aggregation; Affinity propagation clustering; Kappa correlation; Ensemble feature selection

Summary:A small number of features are significantly correlated with classification in high-dimensional data. An ensemble feature selection method based on cluster grouping is proposed in this paper. Classification related features are chosen using a ranking aggregation technique. These features are divided into unrelated groups by an affinity propagation clustering algorithm with a bicor correlation coefficient. Some diversity and distinguishing feature subsets are constructed by randomly selecting a feature from each group and are used to train base classifiers. Finally, some base classifiers that have better classification performance are selected using a kappa coefficient and integrated using a majority voting strategy. The experimental results based on five gene expression datasets show that the proposed method has low classification error rates, stable classification performance and strong scalability in terms of sensitivity, specificity, accuracy and G-Mean criteria. (C) 2016 Elsevier Inc. All rights reserved.