The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier
作者：
Highlights：
• This paper proposes a granular classifier to discover hyperboxes in three phases.
• The first phase of the proposed model uses the set calculus to build the hyperboxes.
• The second phase develops the geometry of hyperboxes using PSO algorithm.
• The PSO is used to optimize the classification rate and expanding the hyperboxes.
• The third phase identifies the noise points to improve the geometry of classifier.
摘要
•This paper proposes a granular classifier to discover hyperboxes in three phases.•The first phase of the proposed model uses the set calculus to build the hyperboxes.•The second phase develops the geometry of hyperboxes using PSO algorithm.•The PSO is used to optimize the classification rate and expanding the hyperboxes.•The third phase identifies the noise points to improve the geometry of classifier.
论文关键词：Hyperbox geometry of classifiers,Granular classifier,Membership functions,Particle swarm optimization,DBSCAN clustering algorithm
论文评审过程：Received 8 October 2014, Revised 11 December 2014, Accepted 15 December 2014, Available online 2 January 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2014.12.017
