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Multi-level fusion of classifiers through fuzzy ensemble learning

Liu, Han and Chen, Shyi-Ming 2018. Multi-level fusion of classifiers through fuzzy ensemble learning. Presented at: 11th International Symposium on Computational Intelligence and Design, Hangzhou, China, 8-9 December 2018.

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Abstract

Classification is a popular task of supervised machine learning, which can be achieved by training a single classifier or a group of classifiers. In general, the performance of each traditional learning algorithm which leads to the production of a single classifier is varied on different data sets, i.e., each learning algorithm may produce good classifiers on some data sets, but may produce poor classifiers on the other data sets. In order to achieve a more stable performance of machine learning, ensemble learning has been undertaken more popularly to produce a group of classifiers that can be complementary to each other. In this paper, we focus on advancing fuzzy classification through multi-level fusion of fuzzy classifiers in the setting of ensemble learning. In particular, we propose an ensemble learning framework that leads to creating a group of fuzzy classifiers that are complementary to each other. The experimental results show that the proposed ensemble learning framework leads to considerable advances in the performance of fuzzy classification, in comparison with using each single fuzzy classifier.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Related URLs:
Date of First Compliant Deposit: 9 January 2019
Last Modified: 15 Oct 2020 01:34
URI: http://orca-mwe.cf.ac.uk/id/eprint/118262

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