Fuzzy integral driven ensemble classification using a priori fuzzy measures

Utkarsh Agrawal, Christian Wagner, Jon Garibaldi, Daniele Soria

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Number of pages7
ISBN (Electronic)9781538617281
ISBN (Print)9781538617298
Publication statusPublished - 10 Oct 2019
Event2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) - New Orleans, United States
Duration: 23 Jun 201926 Jun 2019

Publication series

NameIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISSN (Print)1544-5615
ISSN (Electronic)1558-4739


Conference2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Abbreviated titleFUZZ-IEEE
Country/TerritoryUnited States
CityNew Orleans
Internet address


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