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dc.contributor.authorAgrawal, Utkarsh
dc.contributor.authorWagner, Christian
dc.contributor.authorGaribaldi, Jon
dc.contributor.authorSoria, Daniele
dc.identifier.citationAgrawal , U , Wagner , C , Garibaldi , J & Soria , D 2019 , Fuzzy integral driven ensemble classification using a priori fuzzy measures . in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) . , 8858821 , IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , IEEE , pp. 1-7 , 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , New Orleans , United States , 23/06/19 .
dc.identifier.otherPURE: 263044015
dc.identifier.otherPURE UUID: a3575ef9-41c8-41aa-b403-1ff4aeff3734
dc.identifier.otherScopus: 85073779981
dc.description.abstractAggregation 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.
dc.relation.ispartof2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)en
dc.relation.ispartofseriesIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)en
dc.rightsCopyright © 2019 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectR Medicineen
dc.titleFuzzy integral driven ensemble classification using a priori fuzzy measuresen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen

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