{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:37:45Z","timestamp":1782484665872,"version":"3.54.5"},"reference-count":44,"publisher":"Elsevier","isbn-type":[{"value":"9781558603356","type":"print"}],"license":[{"start":{"date-parts":[[1994,1,1]],"date-time":"1994-01-01T00:00:00Z","timestamp":757382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[1994]]},"DOI":"10.1016\/b978-1-55860-335-6.50023-4","type":"book-chapter","created":{"date-parts":[[2014,7,1]],"date-time":"2014-07-01T02:58:03Z","timestamp":1404183483000},"page":"121-129","source":"Crossref","is-referenced-by-count":1121,"title":["Irrelevant Features and the Subset Selection Problem"],"prefix":"10.1016","author":[{"given":"George H.","family":"John","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ron","family":"Kohavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl","family":"Pfleger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib1","unstructured":"Almuallim, H., and Dietterich, T. G. 1991. Learning with many irrelevant features. In Ninth National Conference on Artificial Intelligence, 547\u2013552. MIT Press."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib2","first-page":"773","article-title":"Use of distance measures, information measures and error bounds in feature evaluation","volume":"volume 2","author":"Ben-Bassat","year":"1982"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib3","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0893-6080(05)80010-3","article-title":"Training a 3-node neural network is NP-complete","volume":"5","author":"Blum","year":"1992","journal-title":"Neural Networks"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib4","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/0020-0190(87)90114-1","article-title":"Occam's razor","volume":"24","author":"Blumer","year":"1987","journal-title":"Information Processing Letters"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib5","series-title":"Optimal Subset Selection","author":"Boyce","year":"1974"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib6","series-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib7","doi-asserted-by":"crossref","unstructured":"Cardie, C. 1993. Using decision trees to improve case-based learning. In Proceedings of the Tenth International Conference on Machine Learning, 25\u201332. Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-307-3.50010-1"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib8","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Freitag, D. 1994. Greedy attribute selection. In Cohen, W. W., and Hirsh, H., eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-335-6.50012-X"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib9","unstructured":"Cohen, W. W. 1993. Efficient pruning methods for separate-and-conquer rule learning systems. In 13th International Joint Conference on Artificial Intelligence, 988\u2013994. Morgan Kaufmann."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib10","series-title":"Pattern Recognition: A Statistical Approach","author":"Devijver","year":"1982"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib11","series-title":"Applied Regression Analysis","author":"Draper","year":"1981"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/0004-3702(89)90046-5","article-title":"Models of incremental concept formation","volume":"40","author":"Gennari","year":"1989","journal-title":"Artificial Intelligence"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib13","unstructured":"Hancock, T. R. 1989. On the difficulty of finding small consistent decision trees. Unpublished Manuscript, Harvard University."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib14","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1022631118932","article-title":"Very simple classification rules perform well on most commonly used datasets","volume":"11","author":"Holte","year":"1993","journal-title":"Machine Learning"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib15","unstructured":"Kira, K., and Rendell, L. A. 1992a. The feature selection problem: Traditional methods and a new algorithm. In Tenth National Conference on Artificial Intelligence, 129\u2013134. MIT Press."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib16","doi-asserted-by":"crossref","unstructured":"Kira, K., and Rendell, L. A. 1992b. A practical approach to feature selection. In Proceedings of the Ninth International Conference on Machine Learning. Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib17","doi-asserted-by":"crossref","unstructured":"Kononenko, I. 1994. Estimating attributes: Analysis and extensions of Relief. In Proceedings of the European Conference on Machine Learning.","DOI":"10.1007\/3-540-57868-4_57"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib18","unstructured":"Langley, P., and Sage, S. 1994. Oblivious decision trees and abstract cases. In Working Notes of the AAAI94 Workshop on Case-Based Reasoning. In press."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib19","unstructured":"Levy, A. Y. 1993. Irrelevance Reasoning in Knowledge Based Systems. Ph.D. Dissertation, Stanford University."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib20","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/BF00116827","article-title":"Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm","volume":"2","author":"Littlestone","year":"1988","journal-title":"Machine Learning"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib21","first-page":"661","volume":"15","author":"Mallows","year":"1973","journal-title":"Some comments on cp. Tech-nometrics"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TIT.1963.1057810","article-title":"On the effectiveness of receptors in recognition systems","volume":"9","author":"Marill","year":"1963","journal-title":"IEEE Transactions on Information Theory"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib23","series-title":"Subset Selection in Regression","author":"Miller","year":"1990"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib24","unstructured":"Modrzejewski, M. 1993. Feature selection using rough sets theory. In Brazdil, P. B., ed., Proceedings of the European Conference on Machine Learning, 213\u2013226."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib25","doi-asserted-by":"crossref","unstructured":"Moore, A. W., and Lee, M. S. 1994. Efficient algorithms for minimizing cross validation error. In Cohen, W. W., and Hirsh, H., eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-335-6.50031-3"},{"issue":"4","key":"10.1016\/B978-1-55860-335-6.50023-4_bib26","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1145\/356893.356898","article-title":"Decision trees and diagrams","volume":"14","author":"Moret","year":"1982","journal-title":"ACM Computing Surveys"},{"issue":"9","key":"10.1016\/B978-1-55860-335-6.50023-4_bib27","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1109\/T-C.1971.223398","article-title":"A comparison of seven techniques for choosing subsets of pattern recognition properties","volume":"C-20","author":"Mucciardi","year":"1971","journal-title":"IEEE Transactions on Computers"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib28","unstructured":"Murphy, P. M., and Aha, D. W. 1994. UCI repository of machine learning databases. For information contact ml-repository@ics.uci.edu."},{"issue":"9","key":"10.1016\/B978-1-55860-335-6.50023-4_bib29","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TC.1977.1674939","article-title":"A branch and bound algorithm for feature subset selection","volume":"C-26","author":"Narendra","year":"1977","journal-title":"IEEE Transactions on Computers"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib30","series-title":"Applied Linear Statistical Models","author":"Neter","year":"1990"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib31","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1023\/A:1022611825350","article-title":"Boolean feature discovery in empirical learning","volume":"5","author":"Pagallo","year":"1990","journal-title":"Machine Learning"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib32","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Machine Learning"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib33","series-title":"C4.5: Programs for Machine Learning","author":"Quinlan","year":"1992"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib34","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1214\/aos\/1176350051","article-title":"Stochastic complexity and modeling","volume":"14","author":"Rissanen","year":"1986","journal-title":"Ann. Statist"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib35","unstructured":"Russel, S. J. 1986. Preliminary steps toward the automation of induction. In Proceedings of the National Conference on Artificial Intelligence, 477\u2013484."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib36","series-title":"The Use of Knowledge in Analogy and Induction","author":"Russel","year":"1989"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib37","doi-asserted-by":"crossref","unstructured":"Schlimmer, J. C. 1993. Efficiently inducing determinations: A complete and systematic search algorithm that uses optimal pruning. In Proceedings of the Tenth International Conference on Machine Learning, 284\u2013290. Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-307-3.50043-5"},{"issue":"2","key":"10.1016\/B978-1-55860-335-6.50023-4_bib38","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1142\/S0218001488000145","article-title":"On automatic feature selection","volume":"2","author":"Siedlecki","year":"1988","journal-title":"International Journal of Pattern Recognition and Artificial Intelligence"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib39","doi-asserted-by":"crossref","unstructured":"Skalak, D. B. 1994. Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Cohen, W. W., and Hirsh, H., eds., Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-335-6.50043-X"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib40","unstructured":"Thrun, S. B., et al. 1991. The monk's problems: A performance comparison of different learning algorithms. Technical Report CMU-CS-91\u2013197, Carnegie Mellon University."},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib41","doi-asserted-by":"crossref","unstructured":"Vafai, H., and De Jong, K. 1992. Genetic algorithms as a tool for feature selection in machine learning. In Fourth International Conference on Tools with Artificial Intelligence, 200\u2013203. IEEE Computer Society Press.","DOI":"10.1109\/TAI.1992.246402"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib42","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1111\/j.2517-6161.1987.tb01695.x","article-title":"Estimation and inference by compact coding","volume":"49","author":"Wallace","year":"1987","journal-title":"Journal of the Royal Statistical Society (B)"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib43","series-title":"Computer Systems that Learn","author":"Weiss","year":"1991"},{"key":"10.1016\/B978-1-55860-335-6.50023-4_bib44","unstructured":"Xu, L.; Yan, P.; and Chang, T. 1989. Best first strategy for feature selection. In Ninth International Conference on Pattern Recognition, 706\u2013708. IEEE Computer Society Press."}],"container-title":["Machine Learning Proceedings 1994"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:B9781558603356500234?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:B9781558603356500234?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T15:43:26Z","timestamp":1746287006000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/B9781558603356500234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1994]]},"ISBN":["9781558603356"],"references-count":44,"URL":"https:\/\/doi.org\/10.1016\/b978-1-55860-335-6.50023-4","relation":{},"subject":[],"published":{"date-parts":[[1994]]}}}