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Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms

Agrawal, Utkarsh and Pinar, Anthony J. and Wagner, Christian and Havens, Timothy C. and Soria, Daniele and Garibaldi, Jonathan M. (2018) Comparison of Fuzzy Integral-Fuzzy Measure based Ensemble Algorithms with the State-of-the-art Ensemble Algorithms. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science . Springer, Cham, Switzerland, pp. 329-341. E-ISBN 978-3-319-91479-4. (doi:10.1007/978-3-319-91473-2_29) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:79610)

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The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-91473-2_29
Uncontrolled keywords: Ensemble classification comparison, Fuzzy measures, Fuzzy Integrals, Adaboost, Bagging, Majority Voting, Random Forest
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Daniel Soria
Date Deposited: 17 Jan 2020 16:10 UTC
Last Modified: 16 Feb 2021 14:10 UTC
Resource URI: (The current URI for this page, for reference purposes)
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