Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach

Hossam M. Zawbaa*, E. Emary, Crina Grosan, Vaclav Snasel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

101 Citations (Scopus)


Selection of a representative set of features is still a crucial and challenging problem in machine learning. The complexity of the problem increases when any of the following situations occur: a very large number of attributes (large dimensionality); a very small number of instances or time points (small-instance set). The first situation poses problems for machine learning algorithm as the search space for selecting a combination of relevant features becomes impossible to explore in a reasonable time and with reasonable computational resources. The second aspect poses the problem of having insufficient data to learn from (insufficient examples). In this work, we approach both these issues at the same time. The methods we proposed are heuristics inspired by nature (in particular, by biology). We propose a hybrid of two methods which has the advantage of providing a good learning from fewer examples and a fair selection of features from a really large set, all these while ensuring a high standard classification accuracy of the data. The methods used are antlion optimization (ALO), grey wolf optimization (GWO), and a combination of the two (ALO-GWO). We test their performance on datasets having almost 50,000 features and less than 200 instances. The results look promising while compared with other methods such as genetic algorithms (GA) and particle swarm optimization (PSO).

Original languageEnglish
Pages (from-to)29-42
Number of pages14
JournalSwarm and Evolutionary Computation
Publication statusPublished - Oct 2018


  • Antlion optimization
  • Bio-inspired optimization
  • Feature selection
  • Grey wolf optimization
  • Hybrid ALO-GWO
  • Swarm optimization

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