Optimising ensemble combination based on maximisation of diversity. Issue 15 (1st July 2017)

• Record Type: Journal Article
• Authors: Mao, Shasha; Lin, Weisi; Chen, Jiawei; Xiong, Lin
• Is Part Of: Electronics letters. Volume 53:Issue 15(2017)
• Page Start: 1042
• Page End: 1044
• Publication Date: 2017-07-01
• Subjects: learning (artificial intelligence) -- optimisation; optimising ensemble combination -- diversity maximisation -- ensemble system -- ensemble learning; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2017.0795
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Balancing diversity and accuracy of individuals is crucial for improving the performance of an ensemble system, since they are two important but incompatible factors for ensemble learning. When multiple individuals are combined with the corresponding weights, the diversity should be dominated by individuals and their weights, whereas the weights are normally ignored in the analysis of diversity in most research. Inspired by this, the authors propose a novel ensemble method which seeks an optimal combination to maximise diversity and accuracy of weighted individuals with the constraint on the minimal ensemble error. Furthermore, a new expression is given based on the generated individuals and their weights to exploit the diversity of an ensemble. Experimental results illustrate that the proposed method outperforms relevant existing methods.