title: Classifier ensemble selection based on affinity propagation clustering.
abstract: 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.
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Meng
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China.
authors.forename: Han
authors.initials: H
authors.lastname: Hao
authors.affiliation: School of Life Science and Biotechnology, Dalian University of Technology, Dalian, Liaoning 116023, China. Electronic address: luanyush@dlut.edu.cn.
authors.forename: Yushi
authors.initials: Y
authors.lastname: Luan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Affinity propagation clustering; Classification; Ensemble feature selection; Kappa correlation; Ranking aggregation