A Study on Classification Problem using Complex Neuro-Fuzzy Approach

• Main Authors: Kit-weng Leong, 梁杰榮
• Other Authors: ChunShien Li
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/tu322j

碩士 === 國立中央大學 === 資訊管理學系 === 103 === We present a complex neuro-fuzzy system (CNFS) as a pattern classifier that utilizes complex fuzzy sets. For feature selection of training samples, we consider the removal of redundant and irrelevant features by which we aspire to improve the predictive accuracy of the classifier. Based on information theory, we employ a well-known feature selection method that combines minimal redundancy and maximal relevance for feature selection. One crucial problem for fuzzy-rule based model construction is that the amount of data is usually large in volume, which would make the consequence part parameters of rule base grow exponentially. A modified grid-partitioning method that can select portioned area of input space if some rule-firing-strength threshold is satisfied is employed to deal with that major problem. For the parameter learning method, the particle swarm optimization algorithm (PSO) and the recursive least-squares estimator (RLSE) are integrated as a hybrid learning method to adjust the free parameters of the CNFS effectively. We conducted experiments using 10 data sets of various fields and made performance comparison with other classifiers. The experimental results demonstrate that our approach can find smaller size feature subset with high classification accuracy.