Paper Info
Title
Minimax Probability TSK Fuzzy System Classifier: A More Transparent and Highly Interpretable Classification Model
Abstract
When an intelligent model is used for medical diagnosis, it is desirable to have a high level of interpretability and transparent model reliability for users. Compared with most of the existing intelligence models, fuzzy systems have shown a distinctive advantage in their interpretabilities. However, how to determine the model reliability of a fuzzy system trained for a recognition task is still an unsolved problem at present. In this study, a minimax probability Takagi-Sugeno- Kang (TSK) fuzzy system classifier, called MP-TSK-FSC, is proposed to train a fuzzy system classifier and determine the model reliability simultaneously. For the proposed MP-TSKFSC, a lower bound of correct classification can be presented to the users to characterize the reliability of the trained fuzzy classifier. Thus, the obtained classifier has the distinctive characteristics of both a high level of interpretability and transparent model reliability inherited from the fuzzy system and minimax probability learning strategy, respectively. Our experiments on synthetic datasets and several real world datasets for medical diagnosis have confirmed the distinctive characteristics of the proposed method.
Year
DOI
Venue
2015
10.1109/TFUZZ.2014.2328014
Fuzzy Systems, IEEE Transactions
Keywords
Field
DocType
fuzzy systems,clustering algorithms,training data,medical diagnosis,reliability,optimization
Interpretability,Neuro-fuzzy,Minimax,Pattern recognition,Fuzzy classification,Computer science,Fuzzy set operations,Artificial intelligence,Fuzzy control system,Cluster analysis,Classifier (linguistics),Machine learning
Journal
Volume
Issue
ISSN
PP
99
1063-6706
Citations 
PageRank 
References 
16
0.53
28
Authors
4
Name
Order
Citations
PageRank
Zhaohong Deng1160.53
Longbing Cao22212185.04
Yizhang Jiang338227.24
Shitong Wang41485109.13