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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F10%3A00351614%21RIV11-GA0-67985807
rdf:type
skos:Concept n19:Vysledek
dcterms:description
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. 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.
dcterms:title
Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure
skos:prefLabel
Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure Dynamic Classifier Aggregation using Fuzzy Integral with Interaction-Sensitive Fuzzy Measure
skos:notation
RIV/67985807:_____/10:00351614!RIV11-GA0-67985807
n4:aktivita
n5:Z n5:P
n4:aktivity
P(GA201/08/0802), P(ME 949), Z(AV0Z10300504)
n4:dodaniDat
n7:2011
n4:domaciTvurceVysledku
n15:6506984 n15:6036627
n4:druhVysledku
n14:D
n4:duvernostUdaju
n20:S
n4:entitaPredkladatele
n8:predkladatel
n4:idSjednocenehoVysledku
255481
n4:idVysledku
RIV/67985807:_____/10:00351614
n4:jazykVysledku
n11:eng
n4:klicovaSlova
dynamic classifier combining; fuzzy integral; Choquet integral; fuzzy measure
n4:klicoveSlovo
n6:fuzzy%20integral n6:fuzzy%20measure n6:dynamic%20classifier%20combining n6:Choquet%20integral
n4:kontrolniKodProRIV
[5C9D5ED330D1]
n4:mistoKonaniAkce
Cairo
n4:mistoVydani
Los Alamitos
n4:nazevZdroje
Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications
n4:obor
n13:IN
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n10:ME%20949 n10:GA201%2F08%2F0802
n4:rokUplatneniVysledku
n7:2010
n4:tvurceVysledku
Štefka, David Holeňa, Martin
n4:typAkce
n17:WRD
n4:zahajeniAkce
2010-11-29+01:00
n4:zamer
n12:AV0Z10300504
s:numberOfPages
6
n18:hasPublisher
IEEE
n21:isbn
978-1-4244-8135-4