@Article{jbd.2021.010364,
AUTHOR = {Shichao Wang, Yu Xue, Weiwei Jia},
TITLE = {A New Population Initialization of Particle Swarm Optimization Method  Based on PCA for Feature Selection},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {1--9},
URL = {http://www.techscience.com/jbd/v3n1/41296},
ISSN = {2579-0056},
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.},
DOI = {10.32604/jbd.2021.010364}
}