A novel classifier ensemble method with sparsity and diversity

• Published in: Neurocomputing (Amsterdam) 2014-06, Vol.134, p.214-221
• Main Authors: Yin, Xu-Cheng, Huang, Kaizhu, Hao, Hong-Wei, Iqbal, Khalid, Wang, Zhi-Bin
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Benchmarking; Classifier ensemble; Classifiers; Computer science; control theory; systems; Connectionism. Neural networks; Data processing. List processing. Character string processing; Diversity learning; Evolution; Exact sciences and technology; Genetic algorithm; Learning; Least squares method; Mathematical analysis; Memory organisation. Data processing; Neural network ensembles; Optimization; Pascal (programming language); Software; Sparsity learning
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.07.054

We consider the classifier ensemble problem in this paper. Due to its superior performance to individual classifiers, class ensemble has been intensively studied in the literature. Generally speaking, there are two prevalent research directions on this, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. While most current approaches are emphasized on either sparsity or diversity only, we investigate the classifier ensemble by learning both sparsity and diversity simultaneously. We manage to formulate the classifier ensemble problem with the sparsity or/and diversity learning in a general framework. In particular, the classifier ensemble with sparsity and diversity can be represented as a mathematical optimization problem. We then propose a heuristic algorithm, capable of obtaining ensemble classifiers with consideration of both sparsity and diversity. We exploit the genetic algorithm, and optimize sparsity and diversity for classifier selection and combination heuristically and iteratively. As one major contribution, we introduce the concept of the diversity contribution ability so as to select proper classifier components and evolve classifier weights eventually. Finally, we compare our proposed novel method with other conventional classifier ensemble methods such as Bagging, least squares combination, sparsity learning, and AdaBoost, extensively on UCI benchmark data sets and the Pascal Large Scale Learning Challenge 2008 webspam data. The experimental results confirm that our approach leads to better performance in many aspects.