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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | S̆tefka, D. Holen̆a, M. |
| Copyright Year | 2010 |
| Description | Author affiliation: Institute of Computer Science, Academy of Sciences of the Czech Republic, v.v.i., Prague, Czech Republic (Holen̆a, M.) |
| Abstract | 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. |
| Starting Page | 225 |
| Ending Page | 230 |
| File Size | 200102 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424481347 |
| e-ISBN | 9781424481361 |
| DOI | 10.1109/ISDA.2010.5687260 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-29 |
| Publisher Place | Egypt |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | dynamic classifier combining Cho-quet integral Additives Atmospheric measurements Density measurement Diversity reception fuzzy measure fuzzy integral Particle measurements Intelligent systems Testing |
| Content Type | Text |
| Resource Type | Article |
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