摘要
针对现有半监督分类方法无法对移动界面模式进行有效分类的问题,提出一种采用改进极限学习机的移动界面模式半监督分类方法。为了提高极限学习机的分类效果,利用改进的粒子群优化算法优化极限学习机的初始参数。根据移动界面模式数据的特点,利用主动学习和模糊C均值聚类提取信息丰富的未标记数据进行训练和标记。利用分类器实现对所有数据的分类。实验结果表明,该分类方法能够对移动界面模式数据进行有效和合理的分类。
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 characteristics 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.
出处
《计算机工程与应用》
CSCD
北大核心
2018年第2期11-19,共9页
Computer Engineering and Applications
基金
国家自然科学基金(No.61272286)
高等学校博士学科点专项科研基金(No.20126101110006)
陕西省工业科技攻关项目(No.2016GY-123)
宁夏高等学校科学技术研究项目(No.NGY2017225)
西北大学科学研究基金(No.15NW31)
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
particle swarm optimization
extreme learning machine
mobile interface pattern
fuzzy c-means clustering
semi-supervised classification