The Optimization of Feature Selection Based on Chaos Clustering Strategy and Niche Particle Swarm Optimization

Longzhen Duan, Shuqing Yang and Dongbo Zhang

Mathematical Problems in Engineering, 2020, vol. 2020, 1-8

Abstract:

With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/3138659.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/3138659.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3138659

DOI: 10.1155/2020/3138659

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().