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

  • Hybrid Artificial Intelligence and Machine Learning Technologies
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Abstract

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.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata, West Bengal, 700032, India

    Manosij Ghosh, Ritam Guha & Ram Sarkar

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, 98071, USA

    Ajith Abraham

Authors
  1. Manosij Ghosh
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  2. Ritam Guha
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  3. Ram Sarkar
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  4. Ajith Abraham
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Correspondence to Ram Sarkar.

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Ghosh, M., Guha, R., Sarkar, R. et al. A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput & Applic 32, 7839–7857 (2020). https://doi.org/10.1007/s00521-019-04171-3

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