Počet záznamů: 1

Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition

  1. 1.
    0365937 - UTIA-B 2012 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
    Somol, Petr - Grim, Jiří - Pudil, P.
    Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition.
    Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011). Piscataway: IEEE, 2011, s. 502-509. ISBN 978-1-4577-0653-0.
    [The 2011 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011). Anchorage, Alaska (US), 09.10.2011-12.10.2011]
    Grant CEP: GA MŠk 1M0572
    Grant ostatní:GA MŠk(CZ) 2C06019
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: feature selection * high dimensionality * ranking * classification * machine learning
    Kód oboru RIV: IN - Informatika
    http://library.utia.cas.cz/separaty/2011/RO/somol-fast dependency-aware feature selection in very-high-dimensional pattern recognition-c.pdf http://library.utia.cas.cz/separaty/2011/RO/somol-fast dependency-aware feature selection in very-high-dimensional pattern recognition-c.pdf

    The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data.
    Trvalý link: http://hdl.handle.net/11104/0201063