HHFS: A Hybrid Hierarchical Feature Selection Method for Ageing Gene Classification

Dehui Li, Quanwang Wu, Mengchu Zhou, Fengji Luo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


As one of the most complicated processes in biological development, ageing remains poorly understood. These days more and more ageing-related gene data sets become available on the Web, where each instance is characterized by a set of hierarchically organized binary features. Traditional data mining methods show limitations in exploiting this hierarchical feature space. This article proposes a hybrid hierarchical feature selection (HHFS) method for classifying genes into prolongevity or anti-longevity ones. HHFS conducts lazy and eager feature selections sequentially, taking into account both uniqueness of a test instance and the whole characteristics of data sets. It adopts two complementary relevancy metrics (i.e., Gini purity and mutual information) to remove hierarchical redundancy. The experiments are conducted based on the ageing-related gene data of four model organisms. The results show that HHFS achieves significantly better prediction performance than several state-of-the-art methods.

Original languageEnglish (US)
Pages (from-to)690-699
Number of pages10
JournalIEEE Transactions on Cognitive and Developmental Systems
Issue number2
StatePublished - Jun 1 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


  • Ageing
  • classification
  • feature selection
  • gene ontology (GO)
  • hierarchical feature space


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