Selection of relevant features and examples in machine learning

by: AL Blum, P Langley

Artificial Intelligence, Vol. 97, No. 1. (December 1997), pp. 245-271.

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Abstract

In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.

@article{blum97selection, abstract = { In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.}, author = {Blum, A. L. and Langley, P. }, citeulike-article-id = {402254}, doi = {10.1016/S0004-3702(97)00063-5}, issn = {0004-3702}, journal = {Artificial Intelligence}, keywords = {dimensionality-reduction, embedded-feature-selection, feature-selection, filter-feature-selection, survey, weighting-feature-selection, wrapper-feature-selection}, month = {December}, number = {1}, pages = {245--271}, posted-at = {2008-04-09 17:22:18}, priority = {5}, title = {Selection of relevant features and examples in machine learning}, url = {http://portal.acm.org/citation.cfm?id=270626}, volume = {97}, year = {1997} }

RIS record

TY - JOUR ID - blum97selection L3 - citeulike-article-id:402254 TI - Selection of relevant features and examples in machine learning JF - Artificial Intelligence VL - 97 IS - 1 SP - 245 EP - 271 SN - 0004-3702 N2 - In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area. KW - dimensionality-reduction KW - embedded-feature-selection KW - feature-selection KW - filter-feature-selection KW - survey KW - weighting-feature-selection KW - wrapper-feature-selection AU - Blum, AL AU - Langley, P PY - 1997/12// UR - http://portal.acm.org/citation.cfm?id=270626 ER -

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