A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification

被引:128
作者
Chakraborty, D [1 ]
Pal, NR [1 ]
机构
[1] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, W Bengal, India
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2004年 / 15卷 / 01期
关键词
classification; feature analysis; neuro-fuzzy systems; rule extraction;
D O I
10.1109/TNN.2003.820557
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neuro-fuzzy scheme for designing a classifier along with feature selection. It is a four-layered. feed-forward network for realizing a fuzzy rule-based classifier. The network is trained by error backpropagation in three phases. In the first phase, the network learns the important features and the classification rules. In the subsequent phases, the network is pruned to an "optimal" architecture that represents an "optimal" set of rules. Pruning is found to drastically reduce the size of the network without degrading the performance. The pruned network is further tuned to improve performance. The rules learned by the network can be easily read from the network. The system is tested on both synthetic and real data sets and found to perform quite well.
引用
收藏
页码:110 / 123
页数:14
相关论文
共 52 条
[1]  
Anderson E., 1935, Bulletin of the American IRIS Society, V59, P2
[2]  
ARENJI HR, 1992, IEEE T NEURAL NETWOR, V3
[3]  
BEZDEKJC, 1999, FUZZY MODELS ALGORIT
[4]   Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm [J].
Chakraborty, D ;
Pal, NR .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2001, 31 (03) :391-400
[5]  
CHIU SL, FUZZY INFORMATION EN, P149
[6]  
Chiu SL., 1994, J INTELL FUZZY SYST, V2, P267, DOI [DOI 10.3233/IFS-1994-2306, 10.3233/IFS-1994-2306]
[7]  
CHIU SL, 1995, P 6 INT FUZZ SYST AS, P1
[8]   Feature analysis: Neural network and fuzzy set theoretic approaches [J].
De, RK ;
Pal, NR ;
Pal, SK .
PATTERN RECOGNITION, 1997, 30 (10) :1579-1590
[9]   NEURAL NETWORKS IN DESIGNING FUZZY-SYSTEMS FOR REAL-WORLD APPLICATIONS [J].
HALGAMUGE, SK ;
GLESNER, M .
FUZZY SETS AND SYSTEMS, 1994, 65 (01) :1-12
[10]  
Haykin S., 1994, Neural networks: a comprehensive foundation