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--- abstract: |- This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training examples and a set of testing examples. The value of the attribute that is to be predicted is known to the learner in the training set, but unknown in the testing set. The theory demonstrates that cross-validation error has two components: error on the training set (inaccuracy) and sensitivity to noise (instability). This general theory is then applied to voting in instance-based learning. Given an example in the testing set, a typical instance-based learning algorithm predicts the designated attribute by voting among the k nearest neighbors (the k most similar examples) to the testing example in the training set. Voting is intended to increase the stability (resistance to noise) of instance-based learning, but a theoretical analysis shows that there are circumstances in which voting can be destabilizing. The theory suggests ways to minimize cross-validation error, by insuring that voting is stable and does not adversely affect accuracy. altloc: - http://extractor.iit.nrc.ca/publications/NRC-35073.pdf chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: [] creators_name: - family: Turney given: Peter D. honourific: '' lineage: '' date: 1994 date_type: published datestamp: 2001-10-13 department: ~ dir: disk0/00/00/18/21 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 1821 fileinfo: /style/images/fileicons/application_pdf.png;/1821/3/NRC%2D35073.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: ~ lastmod: 2011-03-11 08:54:48 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 331-360 pubdom: FALSE publication: Journal of Experimental and Theoretical Artificial Intelligence publisher: ~ refereed: TRUE referencetext: |- Aha, D.W., Kibler, D., & Albert, M.K. (1991) Instance-based learning algorithms, Machine Learning, 6:37-66. Cover, T.M., & Hart, P.E. (1967) Nearest neighbor pattern classification, IEEE Transac-tions on Information Theory, IT-13:21-27. Also in (Dasarathy, 1991). Dasarathy, B.V. (1991) Nearest Neighbor Pattern Classification Techniques, Edited col-lection (California: IEEE Press). Fix, E., & Hodges, J.L. (1951) Discriminatory analysis: nonparametric discrimination: consistency properties, Project 21-49-004, Report Number 4, USAF School of Aviation Medicine, Randolph Field, Texas, 261-279. Also in (Dasarathy, 1991). Fraser, D.A.S. (1976) Probability and Statistics: Theory and Applications (Massachusetts: Duxbury Press). Kibler, D., Aha, D.W., & Albert, M.K. (1989) Instance-based prediction of real-valued attributes, Computational Intelligence, 5:51-57. Langley, P. (1993) Average-case analysis of a nearest neighbor algorithm, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chamb�ry, France, in press. Sakamoto, Y., Ishiguro, M., & Kitagawa, G. (1986) Akaike Information Criterion Statis-tics (Dordrecht, Holland: Kluwer Academic Publishers). Tomek, I. (1976) A generalization of the k-NN rule, IEEE Transactions on Systems, Man, and Cybernetics, SMC-6:121-126. Also in (Dasarathy, 1991). Turney, P.D. (1990) The curve fitting problem: a solution, British Journal for the Philoso-phy of Science, 41:509-530. Turney, P.D. (1993) A theory of cross-validation error. Submitted to the Journal of Exper-imental and Theoretical Artificial Intelligence. Weiss, S.M., & Kulikowski, C.A. (1991) Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems (California: Morgan Kaufmann). relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:41:13 subjects: - comp-sci-art-intel - comp-sci-mach-learn - comp-sci-stat-model succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Theoretical analyses of cross-validation error and voting in instance-based learning type: journalp userid: 2175 volume: 6