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rdf:type
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Description
| - 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.
- 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. (en)
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Title
| - Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition
- Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition (en)
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skos:prefLabel
| - Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition
- Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition (en)
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skos:notation
| - RIV/67985556:_____/11:00365937!RIV12-AV0-67985556
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
| - P(1M0572), Z(AV0Z10750506)
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/67985556:_____/11:00365937
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - feature selection; high dimensionality; ranking; classification; machine learning (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011)
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Grim, Jiří
- Pudil, P.
- Somol, Petr
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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http://linked.open...n/vavai/riv/zamer
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number of pages
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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