Self-adjusting harmony search-based feature selection

Ling Zheng, Ren Diao, Qiang Shen

Research output: Contribution to journalArticlepeer-review

37 Citations (SciVal)
218 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1567-1579
JournalSoft Computing
Issue number6
Publication statusPublished - Jun 2015


  • feature selection
  • harmony memory consolidation
  • pitch adjustment strategy


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