Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00442868" target="_blank" >RIV/67985807:_____/15:00442868 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.fss.2014.09.005" target="_blank" >http://dx.doi.org/10.1016/j.fss.2014.09.005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.fss.2014.09.005" target="_blank" >10.1016/j.fss.2014.09.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures

Popis výsledku v původním jazyce

In classifier aggregation using fuzzy integral, the performance of the classifier system depends heavily on the choice of the underlying fuzzy measure. However, little attention has been given to the choice of the fuzzy measure in the literature; usually, the Sugeno lambda-measure is used. A weakness of the Sugeno lambda-measure is that it cannot model the interactions between individual classifiers. That motivated us to develop two novel fuzzy measures and a modification of an existing fuzzy measure which are interaction-sensitive, i.e., they model not only the confidences of classifiers, but also their mutual similarities. The properties of the measures are first studied theoretically, and in the experimental section, the performance of the proposedmeasures is compared to the traditionally used additive measure and Sugeno lambda-measure. Experiments on 23 benchmark datasets and 3 different classifier systems show that the interaction-sensitive fuzzy measures clearly outperform their

Název v anglickém jazyce

Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures

Popis výsledku anglicky

In classifier aggregation using fuzzy integral, the performance of the classifier system depends heavily on the choice of the underlying fuzzy measure. However, little attention has been given to the choice of the fuzzy measure in the literature; usually, the Sugeno lambda-measure is used. A weakness of the Sugeno lambda-measure is that it cannot model the interactions between individual classifiers. That motivated us to develop two novel fuzzy measures and a modification of an existing fuzzy measure which are interaction-sensitive, i.e., they model not only the confidences of classifiers, but also their mutual similarities. The properties of the measures are first studied theoretically, and in the experimental section, the performance of the proposedmeasures is compared to the traditionally used additive measure and Sugeno lambda-measure. Experiments on 23 benchmark datasets and 3 different classifier systems show that the interaction-sensitive fuzzy measures clearly outperform their

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Fuzzy Sets and Systems

ISSN

0165-0114

e-ISSN

—

Svazek periodika

270

Číslo periodika v rámci svazku

1 July

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

28

Strana od-do

25-52

Kód UT WoS článku

000352208900002

EID výsledku v databázi Scopus

2-s2.0-84926246510