A wrapper-filter feature selection technique based on ant colony optimization

  • Manosij Ghosh
  • Ritam Guha
  • Ram SarkarEmail author
  • Ajith Abraham
Hybrid Artificial Intelligence and Machine Learning Technologies


Ant colony optimization (ACO) is a well-explored meta-heuristic algorithm, among whose many applications feature selection (FS) is an important one. Most existing versions of ACO are either wrapper based or filter based. In this paper, we propose a wrapper-filter combination of ACO, where we introduce subset evaluation using a filter method instead of using a wrapper method to reduce computational complexity. A memory to keep the best ants and feature dimension-dependent pheromone update has also been used to perform FS in a multi-objective manner. Our proposed approach has been evaluated on various real-life datasets, taken from UCI Machine Learning repository and NIPS2003 FS challenge, using K-nearest neighbors and multi-layer perceptron classifiers. The experimental outcomes have been compared to some popular FS methods. The comparison of results clearly shows that our method outperforms most of the state-of-the-art algorithms used for FS. For measuring the robustness of the proposed model, it has been additionally evaluated on facial emotion recognition and microarray datasets.


Wrapper-filter method Ant colony optimization Feature selection NIPS2003 challenge 



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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentJadavpur UniversityKolkataIndia
  2. 2.Scientific Network for Innovation and Research ExcellenceMachine Intelligence Research Labs (MIR Labs)AuburnUSA

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