Effects of feature selection with ‘blurring’ on neurofuzzy systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1160, Issue , 1996, Pages 135-142

  Piramuthu, Selwyn ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION THEORY; LEARNING ALGORITHMS; REGRESSION ANALYSIS;

FEATURE SELECTION METHODS; INFORMATION THEORETIC MEASURE; NEUROFUZZY SYSTEM; STEPWISE MULTIPLE REGRESSION; TARGET CONCEPT;

FEATURE EXTRACTION;

EID: 84949198144     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61863-5_41     Document Type: Conference Paper

References (16)

1. Battiti, R.: Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Transactions on Neural Networks. 5 4 (1994) 537-550
2. Chang, C.-Y.: Dynamic Programming as Applied to Feature Subset Selection in a Pattern Recognition System. IEEE Transactions on Systems. Man, and Cybernetics. 3 2 (1973) 166-171
3. Cover, T. M.: The Best Two Independent Measurements axe Not the Two best. IEEE Transactions on Systems, Man, and Cybernetics. January (1974) 116-117
4. Elashoff, J. D., Elashoff, R. M., and Goldman, G. E.: On the Choice of Variables in Classification Problems with Dichotomous Variables. Biometrika. 54 (1967) 668-670
5. Elomaa, T., and Ukkonen, E.: A Geometric Approach to Feature Selection. Proceedings of the European Conference on Machine Learning. (1994) 351-354
6. Fisher, R. A.: The Use of Multiple Measurements in Taxonomic Problems. Annual Eugenics. 7 II (1936) 179-188
7. Kira, K., and Rendell, L. A.: A Practical Approach to Feature Selection. Proceedings of the Ninth International Conference on Machine Learning. (1992) 249-256
8. Kittler, J.: Mathematical Methods of Feature Selection in Pattern Recognition. International Journal of Man-Machine Studies. 7 (1975) 609-637
9. Malki, H. A., and Moghaddamjoo, A.: Using the Karhunen-Loe’ve Transformation in the Back-Propagation Training Algorithm. IEEE Transactions on Neural Networks. 2 1 (1991) 162-165
10. Meisel, W. S.: Computer-Oriented Approaches to Pattern Recognition. (1972) Academic Press, New York
11. Nauck, D., Klawonn, F., and Kruse, R.: Combining Neural Networks and Fuzzy Controllers. Proceedings of the 8th Austrian AI Conference. (1993) 35-46
12. Piramuthu, S., and Ragavan, H., Improving Connectionist Learning with Symbolic Feature Construction. Connection Science. 1 4 (1992) 33-43
13. Quinlan, J. R.: Decision Trees and Decision Making. IEEE Transactions on Systems, Man and Cybernetics. 20 2 (1990) 339-346
14. Rendell, L., and Ragavan, H.: Improving the Design of Induction Methods by Analyzing Algorithm Fuctionality and Data-Based Concept Complexity. International Joint Conference on Artificial Intelligence. (1993) 952-958
15. The SAS System. SAS Institute Inc. Cary, North Carolina (1995)
16. Toussaint, G. T.: Note on Optimal Selection of Independent Binary-valued Features for Pattern Recognition. IEEE Transactions on Information Theory. 17 (1971) 618