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Self-adjusting harmony search-based feature selection
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AI
DOI：10.1007/s00500-014-1307-8.png)
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摘要
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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.
Keyword:
Feature selection
Harmony search
Harmony memory consolidation
Pitch adjustment strategy
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
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