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A New Population Initialization of Particle Swarm Optimization Method Based on PCA for Feature Selection

Shichao Wang, Yu Xue*, Weiwei Jia

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210000, China

* Corresponding Author: Yu Xue. Email: email

Journal on Big Data 2021, 3(1), 1-9.


In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. In such cases, Feature Selection (FS) is often involved. Meanwhile, owing to their excellent global search ability, evolutionary computation techniques have been widely employed to the FS. So, as a powerful global search method and calculation fast than other EC algorithms, PSO can solve features selection problems well. However, when facing a large number of feature selection, the efficiency of PSO drops significantly. Therefore, plenty of works have been done to improve this situation. Besides, many studies have shown that an appropriate population initialization can effectively help to improve this problem. So, basing on PSO, this paper introduces a new feature selection method with filter-based population. The proposed algorithm uses Principal Component Analysis (PCA) to measure the importance of features first, then based on the sorted feature information, a population initialization method using the threshold selection and the mixed initialization is proposed. The experiments were performed on several datasets and compared to several other related algorithms. Experimental results show that the accuracy of PSO to solve feature selection problems is significantly improved after using proposed method.


Cite This Article

S. Wang, Y. Xue and W. Jia, "A new population initialization of particle swarm optimization method based on pca for feature selection," Journal on Big Data, vol. 3, no.1, pp. 1–9, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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