Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 23, Issue 4, Pages 813-826Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2014.2328014
Keywords
Classification; medical diagnosis; minimax probability decision; Takagi-Sugeno-Kang (TSK) fuzzy system
Funding
- National Natural Science Foundation of China [61170122, 61272210]
- Ministry of education program for New Century Excellent Talents [NCET-120882]
- Fundamental Research Funds for the Central Universities [JUSRP51321B]
- Natural Science Foundation of Jiangsu Province [BK2011003]
- Australian Research Council [DP130102691, LP120100566]
- Australian Research Council [LP120100566] Funding Source: Australian Research Council
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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-TSK-FSC, 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.
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