A new particle swarm feature selection method for classification View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-06

AUTHORS

Kun-Huang Chen, Li-Fei Chen, Chao-Ton Su

ABSTRACT

Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to solve such problems efficiently. This study proposes a regression-based particle swarm optimization for feature selection problem. The proposed algorithm can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms. More... »

PAGES

507-530

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10844-013-0295-y

DOI

http://dx.doi.org/10.1007/s10844-013-0295-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037148402


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