Open Access
ARTICLE
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:
Journal on Big Data 2021, 3(1), 1-9. https://doi.org/10.32604/jbd.2021.010364
Received 15 April 2020; Accepted 20 August 2020; Issue published 25 January 2021
Abstract
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.
Keywords
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. https://doi.org/10.32604/jbd.2021.010364
Citations