Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms
By Bing Xue, Mengjie Zhang, Will N. Browne. Applied Soft Computing, 2014
566 citations recorded by the source corpus.
In classification, feature selection is an important data pre-processing technique, but it is a difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes three new initialisation strategies and three new personal best and global best updating mechanisms in PSO to develop novel feature selection approaches with the goals of maximising the classification performance, minimising the number of features and reducing the computational time. The proposed initialisation strategies and updating mechanisms are compared with the traditional initialisation and the traditional updating mechanism. Meanwhile, the most promising initialisation strategy and updating mechanism are combined to form a new approach (PSO(4-2)) to address feature selection problems and it is compared with two traditional feature selection methods and two PSO based methods. Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. PSO(4-2) outperforms the two traditional methods and two PSO based algorithm in terms of the computational time, the number of features and the classification performance. The superior performance of this algorithm is due mainly to both the proposed initialisation strategy, which aims to take the advantages of both the forward selection and backward selection to decrease the number of features and the computational time, and the new updating mechanism, which can overcome the limitations of traditional updating mechanisms by taking the number of features into account, which reduces the number of features and the computational time.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/ef16846eaefd3e591a1f70e47b03c8e990f11e09
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Citations in the corpus
- A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “[157] developed a new initialization strategy to mimic the typical forward and backward feature selection methods in the PSO search process,”
- Binary ant lion approaches for feature selection (2016, background): “But, when the best feature subset contains a relatively large number of features, backward selection has a larger chance to obtain the best solution [41].”
- A novel chaotic salp swarm algorithm for global optimization and feature selection (2018, methodology): “Authors in [55] proposed a new initialization method and new updating mechanism of PSO.”
- Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “However, canonical PSO has many limitations for feature selection (Xue et al. 2013, 2014).”
- Feature selection in machine learning: A new perspective (2018, background): “Xue [107] proposed a PSO feature selection model with new benefit mechanisms that take into consideration both the classification accuracy and the number of selected features.”
- Particle Swarm Methods (2018, background): “The effect of initialization has been studied in a number of works for different PSO variants [25, 31, 36, 100, 137, 157], offering further insight and suggestions in specific applications.”