A PSO-based rule extractor for medical diagnosis.
One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinson's disease data set.
Department of Computer Science & Information Engineering, National Central University, Taiwan, ROC; Department of Information Technology and Communication, Shih Chien University, Taiwan, ROC; Department of Management and Information Technology, Southern Taiwan University of Science and Technology, Taiwan, ROC.
Yi-Zeng
YZ
Hsieh
Department of Computer Science & Information Engineering, National Central University, Taiwan, ROC. Electronic address: muchun@csie.ncu.edu.tw.
Mu-Chun
MC
Su
Cathay General Hospital, Taiwan, ROC.
Pa-Chun
PC
Wang
D003198:Computer Simulation; D003933:Diagnosis; D017143:Fuzzy Logic; D006801:Humans; D009415:Nerve Net
Fuzzy systems; Neural networks; PSO; Pattern recognition; Rule extraction