{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T22:29:33Z","timestamp":1725488973517},"publisher-location":"Berlin, Heidelberg","reference-count":18,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540410669"},{"type":"electronic","value":"9783540453727"}],"license":[{"start":{"date-parts":[[2000,1,1]],"date-time":"2000-01-01T00:00:00Z","timestamp":946684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2000]]},"DOI":"10.1007\/3-540-45372-5_5","type":"book-chapter","created":{"date-parts":[[2007,8,12]],"date-time":"2007-08-12T03:21:49Z","timestamp":1186888909000},"page":"44-53","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery"],"prefix":"10.1007","author":[{"given":"Marc","family":"Sebban","sequence":"first","affiliation":[]},{"given":"Richard","family":"Nock","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2002,7,18]]},"reference":[{"key":"5_CR1","unstructured":"H. Almuallim and T.G. Dietterich. Learning with many irrelevant features. In Ninth National Conference on Artificial Intelligence, pages 547\u2013552, 1991."},{"key":"5_CR2","unstructured":"L. Breiman, J.H. Friedman, R.A. Olshen, and C.J Stone. Classification And Regression Trees. Chapman & Hall, 1984."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"L.A. Breslow and D.W. Aha. Simplifying decision trees: A survey, to appear inKnowledge Engineering Review, 1997.","DOI":"10.1017\/S0269888997000015"},{"key":"5_CR4","unstructured":"C.E. Brodley and M.A. Friedl. Identifying and eliminating mislabeled training instances. In Thirteen National Conference on Artificial Intelligence, 1996."},{"key":"5_CR5","unstructured":"K.J. Cherkauer and J.W. Shavlik. Growing simpler decision trees to facilitate knowledge discovery. In Second International Conference on Knowledge Discovery and Data Mining, 1996."},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"G.W. Gates. The reduced nearest neighbor rule. IEEE Trans. Inform. Theory, pages 431\u2013433, 1972.","DOI":"10.1109\/TIT.1972.1054809"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"P. E. Hart. The condensed nearest neighbor rule. IEEE Trans. Inform. Theory, pages 515\u2013516, 1968.","DOI":"10.1109\/TIT.1968.1054155"},{"key":"5_CR8","unstructured":"G. H. John. Robust decision trees: Removing outliers from databases. In First International Conference on Knowledge Discovery and Data Mining, pages 174\u2013179, 1995."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"G.H. John, R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem. In Eleventh International Conference on Machine Learning, pages 121\u2013129, 1994.","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"M.J. Kearns and Y. Mansour. On the boosting ability of top-down decision tree learning algorithms. Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, pages 459\u2013468, 1996.","DOI":"10.1145\/237814.237994"},{"key":"5_CR11","first-page":"81","volume":"1","author":"J.R. Quinlan","year":"1986","unstructured":"J.R. Quinlan. Induction of decision trees.Machine Learning, 1:81\u2013106, 1986","journal-title":"Machine Learning"},{"key":"5_CR12","unstructured":"J.R. Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann, 1993."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Annual ACM Conference on Computational Learning Theory, pages 80\u201391, 1998.","DOI":"10.1145\/279943.279960"},{"key":"5_CR14","unstructured":"M. Sebban and R. Nock. Combining feature and example pruning by uncertainty minimization. In Sixteenth Conference on Uncertainty in Artificial Intelligence, 2000."},{"key":"5_CR15","unstructured":"M. Sebban and R. Nock. Instance pruning as an information preserving problem. In Seventeenth International Conference on Machine Learning, 2000."},{"key":"5_CR16","unstructured":"Thrun ET AL. The monk\u2019s problem: a performance comparison of different learning algorithms. Technical reportCMU-CS 91-197-Carnegie Mellon University, 1991."},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"P.E. Utgoff. An improved algorithm for incremental induction of decision trees. In Eleventh Iternationla Conference on Machine Learning, pages 318\u2013325, 1994.","DOI":"10.1016\/B978-1-55860-335-6.50046-5"},{"key":"5_CR18","unstructured":"D.R. Wilson and T.R. Martinez. Instance pruning techniques. In Fourteenth International Conference on Machine Learning, pages 404\u2013411, 1997"}],"container-title":["Lecture Notes in Computer Science","Principles of Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/3-540-45372-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,19]],"date-time":"2019-05-19T10:20:39Z","timestamp":1558261239000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/3-540-45372-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2000]]},"ISBN":["9783540410669","9783540453727"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/3-540-45372-5_5","relation":{},"ISSN":["0302-9743"],"issn-type":[{"type":"print","value":"0302-9743"}],"subject":[],"published":{"date-parts":[[2000]]},"assertion":[{"value":"18 July 2002","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}