Selection of relevant features and examples in machine learning. Blum, A. L. & Langley, P. Artificial Intelligence, 97(1):245-271, 1997. Relevance![Selection of relevant features and examples in machine learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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{BLUM1997245,
title = {Selection of relevant features and examples in machine learning},
journal = {Artificial Intelligence},
volume = {97},
number = {1},
pages = {245-271},
year = {1997},
note = {Relevance},
issn = {0004-3702},
doi = {https://doi.org/10.1016/S0004-3702(97)00063-5},
url = {https://www.sciencedirect.com/science/article/pii/S0004370297000635},
author = {Avrim L. Blum and Pat Langley},
keywords = {Relevant features, Relevant examples, Machine learning},
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.}
}
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