Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure

0351614 - ÚI 2011 RIV US eng C - Conference Paper (international conference)
Štefka, David - Holeňa, Martin
Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure.
Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications. Los Alamitos: IEEE, 2010 - (Hassanien, A.; Abraham, A.; Marcelloni, F.; Hagras, H.; Antonelli, M.; Hong, T.), s. 225-230. ISBN 978-1-4244-8135-4.
[ISDA 2010. International Conference on Intelligent Systems Design and Applications /10./. Cairo (EG), 29.11.2010-01.12.2010]
R&D Projects: GA MŠMT ME 949; GA ČR GA201/08/0802
Institutional research plan: CEZ:AV0Z10300504
Keywords : dynamic classifier combining * fuzzy integral * Choquet integral * fuzzy measure
Subject RIV: IN - Informatics, Computer Science

In classifier combining, predictions of several classifiers are aggregated into a single prediction in order to improve the classification quality. Among others, fuzzy integrals are commonly used as aggregation operators. Usually, Sugeno lambda-measure is used as the fuzzy measure of the integral. However, interaction between the classifiers in the team (diversity), an important property in classifier combining, cannot be modeled by such fuzzy measure. In this paper, we present an interaction-sensitive fuzzy measure (ISFM), which can incorporate the diversity of the team into the aggregation process. Experimental results on 27 datasets show that the Choquet integral w.r.t. the ISFM outperforms the Choquet integral w.r.t. the Sugeno-lambda measure.
Permanent Link: http://hdl.handle.net/11104/0191326