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

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TypeArticle
ConferenceProceedings of the Workshop on Learning in Context-Sensitive Domains,at the 13th International Conference on Machine Learning (ICML-96), July 3-6, 1996., Bari, Italy
AbstractA 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 afinite 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 ofthe 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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number39222
NPARC number8913933
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Record identifier9e8d83c0-f184-4786-b3fe-6d9613a0c01b
Record created2009-04-22
Record modified2016-05-09
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