Iteratively selecting feature subsets for mining from high-dimensional databases

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

Volumn 2431 LNAI, Issue , 2002, Pages 361-372

  Mamitsuka, Hiroshi ^a  

^a KYOTO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATABASE SYSTEMS; FEATURE EXTRACTION; SET THEORY; VIRTUAL REALITY;

DATA MINING METHODS; EFFECT OF NOISE; FEATURE SUBSET; FEATURE SUBSET SELECTION METHODS; HIGH-DIMENSIONAL; MULTIPLE HYPOTHESIS; NOISE LEVELS; PREDICTIVE ACCURACY;

ITERATIVE METHODS;

EID: 84864838607     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45681-3_30     Document Type: Conference Paper

References (12)

1. Breiman, L.: Pasting Small Votes for Classification in Large Databases and On-line. Machine Learning 36 (1999) 85-103
2. Joachims, T. Making Large-scale SVM Learning Practical. In: Scholkopf, B., Burges, C., Smola, A. (eds.): Advances in Kernel Methods - Support Vector Learning, B. MIT Press, Cambridge (1999) 363, 366
3. Kohavi, R., John, G. H.: Wrappers for Feature Subset Selection. Artificial Intelligence 97 (1997) 273-324 362
4. Koller, D., Sahami, M.: Toward Optimal Feature Selection. In: Saitta, L. (eds.): Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann, Bari, Italy (1996) 284-292 362, 365
5. Kononenko, I., Hong, S. J.: Attribute Selection for Modelling. Future Generation Computer Systems 13 (1997) 181-195 362
6. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery Data Mining. Kluwer Academic Publishers, Boston (1998) 362
7. Mamitsuka, H., Abe, N.: Efficient Mining from Large Databases by Query Learning. In: Langley, P. (eds.): Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann, Stanford Univ., CA (2000) 575-582 364
8. Ng, A.: On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples. In: Shavlik, J. (eds.): Proceedings of the Fifteenth Intenational Conference on Machine Learning. Morgan Kaufmann, Madison, WI (1998) 404-412 362
9. Provost, F., Kolluri, V.: A Survey of Methods for Scaling up Inductive Algorithms. Knowledge Discovery and Data Mining 3 (1999) 131-169 362
10. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993) 363, 366
11. Seung, H. S., Opper, M., Sompolinsky, H.: Query by Committee. In: Haussler, D. (eds.): Proceedings of the Fifth Intenational Conference on Computational Learning Theory. Morgan Kaufmann, NY (1992) 287-294 362
12. Xing, E. P., Jordan, M. I., Karp, R. M.: Feature Selection for High-dimensional Genomic Microarray Data In: Brodley, C. E., Danyluk, A. P. (eds.): Proceedings of the Eighteenth Intenational Conference on Machine Learning. Morgan Kaufmann, Madison, WI (2001) 601-608