--- abstract: "A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi- dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task of the learning algorithm is to induce a concept from the training examples. In this paper, we formally distinguish three types of features: primary, contextual, and irrelevant features. We also formally define what it means for one feature to be context-sensitive to another feature. Context-sensitive features complicate the task of the learner and potentially impair the learner's performance. Our formal definitions make it possible for a learner to automatically identify context-sensitive features. After context-sensitive features have been identified, there are several strategies that the learner can employ for managing the features; however, a discussion of these strategies is outside of the scope of this paper. The formal definitions presented here correct a flaw in previously proposed definitions. We discuss the relationship between our work and a formal definition of relevance. \n\n" altloc: - http://extractor.iit.nrc.ca/publications/NRC-39222.pdf chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: Workshop on Learning in Context-Sensitive Domains at the 13th International Conference on Machine Learning (ICML-96) confloc: 'Bari, Italy' contact_email: ~ creators_id: [] creators_name: - family: Turney given: Peter honourific: '' lineage: '' date: 1996 date_type: published datestamp: 2001-11-11 department: ~ dir: disk0/00/00/18/66 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 1866 fileinfo: /style/images/fileicons/application_pdf.png;/1866/3/NRC%2D39222.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 08:54:49 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 53-59 pubdom: FALSE publication: ~ publisher: ~ refereed: TRUE referencetext: |- Bergadano, F., Matwin, S., Michalski, R.S., and Zhang, J. (1992). Learning two-tiered descriptions of flexible concepts: The POSEIDON system. Machine Learning, 8, 5-43. John, G.H., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem, Machine Learning: Proceedings of the Eleventh International Con-ference, pp. 121-129, California: Morgan Kaufmann. Katz, A.J., Gately, M.T., and Collins, D.R. (1990). Robust classifiers without robust features, Neural Computation, 2, 472-479. Michalski, R.S. (1987). How to learn imprecise concepts: A method employing a two-tiered knowledge representa-tion for learning. Proceedings of the Fourth International Workshop on Machine Learning, pp. 50-58, California: Morgan Kaufmann. Pratt, L.Y., Mostow, J, and Kamm, C.A. (1991). Direct transfer of learned information among neural networks. Proceedings of the 9th National Conference on Artificial Intelligence (AAAI-91), pp. 584-580, Anaheim, California. Turney, P.D. (1993a). Exploiting context when learning to classify. In Proceedings of the European Conference on Machine Learning, ECML-93, pp. 402-407. Vienna, Austria: Springer-Verlag. Turney, P.D. (1993b). Robust classification with context-sensitive features. In Industrial and Engineering Applica-tions of Artificial Intelligence and Expert Systems, IEA/ AIE-93, pp. 268-276. Edinburgh, Scotland: Gordon and Breach. Watrous, R.L. (1991). Context-modulated vowel discrimi-nation using connectionist networks. Computer Speech and Language, 5, 341-362. Widmer, G. and Kubat, M. (1992). Learning flexible concepts from streams of examples: FLORA2. In Pro-ceedings of the 10th European Conference on Artificial Intelligence (ECAI-92), Vienna. Chichester: Wiley and Sons. Widmer, G. and Kubat, M. (1993). Effective learning in dynamic environments by explicit context tracking. In Proceedings of the European Conference on Machine Learning (ECML-93), 227-243, Vienna, Austria. Berlin: Springer Verlag. relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:41:22 subjects: - comp-sci-art-intel - comp-sci-mach-learn - comp-sci-stat-model succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'The identification of context-sensitive features: A formal definition of context for concept learning' type: confpaper userid: 2175 volume: ~