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Soft Computing
Erschienen in: Soft Computing 6/2015

01.06.2015 | Focus

Self-adjusting harmony search-based feature selection

verfasst von: Ling Zheng, Ren Diao, Qiang Shen

Erschienen in: Soft Computing | Ausgabe 6/2015

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Abstract

Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. The development of nature-inspired stochastic search techniques allows multiple good quality feature subsets to be discovered without resorting to exhaustive search. In particular, harmony search is a recently developed technique mimicking musicians’ experience, which has been effectively utilised to cope with feature selection problems. In this paper, a self-adjusting approach is proposed for feature selection with an aim to further enhance the performance of the existing harmony search-based method. This novel approach includes three dynamic strategies: restricted feature domain, harmony memory consolidation, and pitch adjustment. Systematic experimental evaluations using high dimensional, real-valued benchmark data sets are conducted in order to verify the efficacy of the proposed work.
Vorheriger Artikel Double-phase locality-sensitive hashing of neighborhood development for multi-relational data
Nächster Artikel Applications and design of cooperative multi-agent ARN-based systems

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Metadaten
Titel
Self-adjusting harmony search-based feature selection
verfasst von
Ling Zheng
Ren Diao
Qiang Shen
Publikationsdatum
01.06.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 6/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1307-8

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