Comparison of classifier-specific feature selection algorithms

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

Volumn 1876 LNCS, Issue , 2000, Pages 677-686

  Kudo, Mineichi ^a   Somol, Petr ^b   Pudil, Pavel ^b   Shimbo, Masaru ^a   Sklansky, Jack ^c  

^a HOKKAIDO UNIVERSITY   (Japan)

^b INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; POPULATION STATISTICS; ALGORITHMS; PATTERN RECOGNITION;

ADAPTIVE VERSIONS; COMPARISON OF CLASSIFIERS; COMPUTATION TIME; FEATURE SELECTION ALGORITHM; FLOATING SEARCHES; LARGE-SCALE PROBLEM; POPULATION SIZES;

GENETIC ALGORITHMS;

EID: 33244468970     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44522-6_70     Document Type: Conference Paper

References (13)

1. Kittler, J.: Feature set search algorithms. In: Chen, C. H. (ed.): Pattern Recognition and Signal Processing, Sijthoff and Noordhoff, Alphen aan den Rijn, Netherlands (1978) 41-60
2. Ferri, F. J., Pudil, P., Hatef, M. and Kittler, J.: Comparative study of techniques for large-scale feature selection. In: Gelsema, E. S. and Kanal, L. N. (eds.): Pattern Recognition in Practice IV, Elsevier Science B. V. (1994) 403-413
3. Kudo, M. and Shimbo, M.: Feature selection based on the structural indices of categories. Pattern Recognition 26 (1993) 891-901
4. Pudil, P., Novovičová, J. and Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15 (1994) 1119-1125
5. Pudil, P., Ferri, F. J., Novovičová, J. and Kittler, J.: Floating search methods for feature selection with nonmonotonic criterion functions. In: 12th International Conference on Pattern Recognition (1994) 279-283
6. Novovičová, J., Pudil, P. and Kittler, J.: Divergence based feature selection for mulimodal class densities. IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (1996) 218-223
7. Holz, H. J. and Loew, M. H.: Relative feature importance: A classifier-independent approach to feature selection. In: Gelsema, E. S. and Kanal, L. N. (eds.), Pattern Recognition in Practice IV, Amsterdam: Elsevier (1994) 473-487
8. Jain, A. and Zongkerm, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Machine Intell 19 (1997) 153-157
9. Kudo, M. and Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33 (2000) 25-41
10. Somol, P., Pudil, P., Novovičová, J. and Paclík, P.: Adaptive floating search methods in feature selection. Pattern Recognition Letters 20 (1999) 1157-1163
11. Vriesenga, M. R.: Genetic Selection and Neureal Modeling for Designing Pattern Classifier. Doctor thesis, University of California, Irvine (1995)
12. Siedlecki, W. and Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10 (1989) 335-347
13. Murphy, P. M. and Aha, D. W.: UCI Repository of machine learning databases [Machine-readable data repository]. University of California, Irivne, Department of Information amd Computation Science (1996)