计算机工程与应用2018，Vol.54Issue(2)：11-19,9.DOI:10.3778/j.issn.1002-8331.1710-0266
改进极限学习机的移动界面模式半监督分类
摘要
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 optimiza-tion 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.Experi-mental results show that the proposed semi-supervised classification method can classify the mobile interface pattern data effectively and reasonably.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Key words
particle swarm optimization/extreme learning machine/mobile interface pattern/fuzzy c-means clustering/semi-supervised classification分类
信息技术与安全科学引用本文复制引用
贾伟,华庆一,张敏军,陈锐,姬翔,王博..改进极限学习机的移动界面模式半监督分类[J].计算机工程与应用,2018,54(2):11-19,9.基金项目
国家自然科学基金(No.61272286) （No.61272286）
高等学校博士学科点专项科研基金(No.20126101110006) （No.20126101110006）
陕西省工业科技攻关项目(No.2016GY-123) （No.2016GY-123）
宁夏高等学校科学技术研究项目(No.NGY2017225) （No.NGY2017225）
西北大学科学研究基金(No.15NW31). （No.15NW31）
