Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms
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 agg...
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| Format: | Conference or Workshop Item |
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2018
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| Online Access: | https://eprints.nottingham.ac.uk/52131/ |
| _version_ | 1848798654621548544 |
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| author | Agrawal, Utkarsh Pinar, Anthony J. Wagner, Christian Havens, Timothy C. Soria, Daniele Garibaldi, Jonathan M. |
| author_facet | Agrawal, Utkarsh Pinar, Anthony J. Wagner, Christian Havens, Timothy C. Soria, Daniele Garibaldi, Jonathan M. |
| author_sort | Agrawal, Utkarsh |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:23:13Z |
| format | Conference or Workshop Item |
| id | nottingham-52131 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:23:13Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-521312020-05-04T19:40:04Z https://eprints.nottingham.ac.uk/52131/ Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms Agrawal, Utkarsh Pinar, Anthony J. Wagner, Christian Havens, Timothy C. Soria, Daniele Garibaldi, Jonathan M. 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. 2018-06-11 Conference or Workshop Item PeerReviewed Agrawal, Utkarsh, Pinar, Anthony J., Wagner, Christian, Havens, Timothy C., 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: 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2018), 11-15 June 2018, Cadiz, Spain. Ensemble Classification Comparison Fuzzy Measures Fuzzy Integrals Adaboost Bagging Majority Voting and Random Forest |
| spellingShingle | Ensemble Classification Comparison Fuzzy Measures Fuzzy Integrals Adaboost Bagging Majority Voting and Random Forest Agrawal, Utkarsh Pinar, Anthony J. Wagner, Christian Havens, Timothy C. Soria, Daniele Garibaldi, Jonathan M. Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| title | Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| title_full | Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| title_fullStr | Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| title_full_unstemmed | Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| title_short | Comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| title_sort | comparison of fuzzy integral-fuzzy measure based ensemble algorithms with the state-of-the-art ensemble algorithms |
| topic | Ensemble Classification Comparison Fuzzy Measures Fuzzy Integrals Adaboost Bagging Majority Voting and Random Forest |
| url | https://eprints.nottingham.ac.uk/52131/ |