基于集成学习和分层结构的多分类算法  

Multi-class Classification Algorithm Based on Ensemble Learning and Hierarchical Structure

作　　者：邹权[1,2,3] 宋莉[1] 陈文强[1] 曾建沧 林琛[1,2] 

机构地区：[1]厦门大学信息科学与技术学院,厦门361005 [2]厦门大学深圳研究院,深圳518057 [3]天津大学计算机科学与技术学院,天津300072

出　　处：《模式识别与人工智能》2015年第9期781-787,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61370010;61102136);国家社会科学基金项目(No.13&ZD148);福建省自然科学基金项目(No.2014J01253);深圳市科技创新基础研究项目(No.JCYJ20120618155655087)资助

摘　　要：分类是数据挖掘、模式识别等领域的重要研究内容.文中提出基于集成学习和分层结构的多分类算法.首先依据问题的类别层分解问题,定义层次分类器的分层结构,然后在分层结构的基础上通过集成学习方法集成多个弱分类器以构成分类过程.在CCDM 2014数据挖掘竞赛中,文中算法在平均精度和F1-score等多项指标上均取得最高成绩,证明该算法在分类问题上的可行性.The classification algorithm is an important research field in data mining and pattern recognition. A multi-class classification algorithm based on ensemble learning and hierarchical structure is proposed. Firstly, the problems are decomposed according to their hierarchy categories. The hierarchical structure of the hierarchical classifier is defined. Then, multiple weak classifiers are integrated by ensemble learning methods based on the hierarchical structure. Thus, the classification process is completed. In the data mining competition of CCDM 2014 , the proposed algorithm achieves the highest performance on several indexes, including average accuracy and F1-score. The results verify the feasibility on the classification problem.

关 键 词：多分类 集成学习 层次分类器 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]