creators_name: Turney, Peter
type: confpaper
datestamp: 2001-11-11
lastmod: 2011-03-11 08:54:49
metadata_visibility: show
title: The identification of context-sensitive features: A formal definition of context for concept learning
ispublished: pub
subjects: comp-sci-art-intel
subjects: comp-sci-mach-learn
subjects: comp-sci-stat-model
full_text_status: public
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. 


date: 1996
date_type: published
pagerange: 53-59
refereed: TRUE
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citation:   Turney, Peter  (1996) The identification of context-sensitive features: A formal definition of context for concept learning.  [Conference Paper]     
document_url: http://cogprints.org/1866/3/NRC-39222.pdf