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|Title:||Optimising ensemble combination based on maximisation of diversity||Authors:||Mao, Shasha
|Issue Date:||2017||Source:||Mao, S., Lin, W., Chen, J., & Xiong, L. (2017). Optimising ensemble combination based on maximisation of diversity. Electronics Letters, 53(15), 1042-1044. doi: 10.1049/el.2017.0795||Series/Report no.:||Electronics Letters||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.||URI:||https://hdl.handle.net/10356/88377
|ISSN:||0013-5194||DOI:||10.1049/el.2017.0795||Rights:||© 2017 Institution of Engineering and Technology (IET). This paper was published in Electronics Letters and is made available as an electronic reprint (preprint) with permission of Institution of Engineering and Technology (IET). The published version is available at: [http://dx.doi.org/10.1049/el.2017.0795]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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