Počet záznamů: 1

Improving feature selection process resistance to failures caused by curse-of-dimensionality effects

  1. 1.
    0368741 - UTIA-B 2012 RIV CZ eng J - Článek v odborném periodiku
    Somol, Petr - Grim, Jiří - Novovičová, Jana - Pudil, P.
    Improving feature selection process resistance to failures caused by curse-of-dimensionality effects.
    Kybernetika. Roč. 47, č. 3 (2011), s. 401-425 ISSN 0023-5954
    Grant CEP: GA MŠk 1M0572; GA ČR GA102/08/0593
    Grant ostatní: GA MŠk(CZ) 2C06019
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: feature selection * curse of dimensionality * over-fitting * stability * machine learning * dimensionality reduction
    Kód oboru RIV: IN - Informatika
    Impakt faktor: 0.454, rok: 2011
    http://library.utia.cas.cz/separaty/2011/RO/somol-0368741.pdf http://library.utia.cas.cz/separaty/2011/RO/somol-0368741.pdf

    The purpose of feature selection in machine learning is at least two-fold – saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality – feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem.
    Trvalý link: http://hdl.handle.net/11104/0203004
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