---
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: ~