The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning

dc.creatorTurney, Peter D.
dc.date2002-12-12
dc.date.accessioned2026-07-07T03:19:17Z
dc.date.available2026-07-07T03:19:17Z
dc.descriptionA 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.
dc.description7 pages
dc.identifierhttps://arxiv.org/abs/cs/0212038
dc.identifierhttp://arxiv.org/abs/cs/0212038
dc.identifier13th International Conference on Machine Learning, Workshop on Learning in Context-Sensitive Domains, Bari, Italy, (1996), 53-59
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31397
dc.subjectMachine Learning
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.2.6; I.5.2
dc.titleThe Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning
dc.typetext

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