关键词: 粒子群优化, 极限学习机, 移动界面模式, 模糊[C]均值聚类, 半监督分类

Abstract: Focused on the issue that the existing semi-supervised classification method cannot effectively classify mobile interface patterns, a semi-supervised classification of mobile interface pattern using improved extreme learning machine is proposed. Firstly, to enhance the classification effect of extreme learning machine, an improved particle swarm optimization algorithm is used to optimize the initial parameters of extreme learning machine. Secondly, according to the characteri-
stics of mobile interface pattern data, active learning and fuzzy c-means clustering are employed to extract information rich unlabeled data for training and labeling. Finally, mobile interface pattern data are classified by using classifier. Experimental results show that the proposed semi-supervised classification method can classify the mobile interface pattern data effectively and reasonably.

Key words: particle swarm optimization, extreme learning machine, mobile interface pattern, fuzzy c-means clustering, semi-supervised classification