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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Classification
Confidence Of Fuzzy Rule-Based Classifiers |
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Authors: |
Tomoharu Nakashima, Ashish Ghosh |
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Published in:
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(2011).ECMS
2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho. European Council for Modeling and Simulation. doi:10.7148/2011 ISBN:
978-0-9564944-2-9 25th
European Conference on Modelling and Simulation, Jubilee Conference Krakow,
June 7-10, 2011
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Citation
format: |
Nakashima, T., & Ghosh, A. (2011). Classification Confidence Of Fuzzy
Rule-Based Classifiers. ECMS 2011 Proceedings edited by: T. Burczynski, J. Kolodziej, A. Byrski, M. Carvalho
(pp. 466-471). European Council for Modeling and Simulation. doi:10.7148/2011-0466-0471 |
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DOI: |
http://dx.doi.org/10.7148/2011-0466-0471 |
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Abstract: |
In this
paper we first introduce the concept of classifica-
tion confidence in fuzzy rule-based classification.
Clas- sification
confidence shows the strength of classification for an unseen pattern. Low classification
confidence for an unseen pattern means that the classification of that pat-
tern is not very clear compared to that with high clas-
sification confidence. Then we focus on the minimum
classification confidence for fuzzy rule-based classifiers using the
classification confidence. The minimum clas- sification confidence represents the worst classification
among given training patterns. Some discussion on as- signing a weight to
training pattern is given to show that cost-sensitive fuzzy rule-based
classifiers are advanta- geous
for producing a large minimum-confidence clas- sifiers. A series of experiments are done in order to
show that reasonable classification boundaries can
be obtained by cost-sensitive fuzzy rule-based classifiers if appropri- ate weights are assigned to training patterns. |
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