A particle swarm optimization based simultaneous learning framework for clustering and classification
作者：
Highlights：
• A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed.
• The simultaneous frame consists of two parts: clustering section and classification section.
• An automatic clustering algorithm is used to find the proper number of clusters.
• An improved particle swarm optimization with a global factor is used in the training phase.
• The performance of PSOSLCC has been extensively compared with four state-of-the-art classification algorithms over a test suit of datasets and texture image segmentation.
摘要
•A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed.•The simultaneous frame consists of two parts: clustering section and classification section.•An automatic clustering algorithm is used to find the proper number of clusters.•An improved particle swarm optimization with a global factor is used in the training phase.•The performance of PSOSLCC has been extensively compared with four state-of-the-art classification algorithms over a test suit of datasets and texture image segmentation.
论文关键词：Classification,Particle swarm optimization,Clustering,Image segmentation,Global factor
论文评审过程：Received 27 November 2012, Revised 30 September 2013, Accepted 20 December 2013, Available online 4 January 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.12.010
