Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures

Agrawal, Utkarsh; Wagner, Christian; Garibaldi, Jonathan M.; Soria, Daniele

Abstract

Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. However, FIs suffer from the potential drawback of not fusing information according to the intuitively interpretable FM, leading to non-intuitive results. The latter is particularly relevant when a FM has been defined using external information (e.g. experts). In order to address this and provide an alternative to the FI, the Recursive Average (RAV) aggregation operator was recently proposed which enables intuitive data fusion in respect to a given FM. With an alternative fusion operator in place, in this paper, we define the concept of 'A Priori' FMs which are generated based on external information (e.g. classification accuracy) and thus provide an alternative to the traditional approaches of learning or manually specifying FMs. We proceed to develop one specific instance of such an a priori FM to support the decision level fusion step in ensemble classification. We evaluate the resulting approach by contrasting the performance of the ensemble classifiers for different FMs, including the recently introduced Uriz and the Sugeno λ-measure; as well as by employing both the Choquet FI and the RAV as possible fusion operators. Results are presented for 20 datasets from machine learning repositories and contextualised to the wider literature by comparing them to state-of-the-art ensemble classifiers such as Adaboost, Bagging, Random Forest and Majority Voting.

Citation

Agrawal, U., Wagner, C., Garibaldi, J. M., & Soria, D. (2019). Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures. In 2019 IEEE International Conference on Fuzzy Systems (1-7). https://doi.org/10.1109/FUZZ-IEEE.2019.8858821

• Conference Name: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
• Conference Location: New Orleans, LA, USA
• Start Date: Jun 23, 2019
• End Date: Jun 26, 2019
• Acceptance Date: Mar 7, 2019
• Online Publication Date: Oct 11, 2019
• Publication Date: 2019-06
• Deposit Date: Sep 19, 2019
• Publicly Available Date: Sep 19, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-7
• Series ISSN: 1558-4739
• Book Title: 2019 IEEE International Conference on Fuzzy Systems
• ISBN: 978-1-5386-1729-8
• DOI: https://doi.org/10.1109/FUZZ-IEEE.2019.8858821
• Public URL: https://nottingham-repository.worktribe.com/output/2635158
• Publisher URL: https://ieeexplore.ieee.org/document/8858821
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.