摘要
层次分类方法利用类别层次结构来分解问题和组织分类器,可有效解决多类分类问题.依据是否要求类别之间存在显式层次关系,层次分类方法可分为两大类.文中对不要求类别之间存在显式层次关系的层次分类方法进行综述.首先归纳和阐述此类方法所采用的基本框架,然后介绍和分析其中若干关键技术的研究进展,最后从算法和应用两个角度对国内外相关研究进行详细叙述,进而对现有方法进行总结,并给出进一步研究的方向.
Hierarchical classification （HC）, decomposing problem and organizing the classifiers according to the category hierarchy, is an efficient solution for multi-class classification problem. Depending on whether an explicit hierarchical relationship among categories is required, HC methods can be divided into two types. In this paper, the HC methods which do not require explicit hierarchical relationship among categories are reviewed systematically. Firstly, the basic framework of this type of methods is outlined. Then, the research progresses of several key techniques are elaborated and analyzed. Next, the related research work at home and abroad is described in detail from both algorithm and application perspectives. Finally, the existing methods are summarized and several future research directions are pointed out.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第12期1130-1139,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60775015
61125305
61233011)
江苏省自然科学基金项目(No.BK20131351)
高等学校学科创新引智计划项目(No.B13022)
江苏高校优势学科建设工程项目
江苏省青蓝工程项目
中央高校基本科研业务费专项资金项目(No.30920130122005
30920130122006
30920130121004)资助
关键词
层次分类
多类分类
类别层次
特征融合
图像分类
Hierarchical Classification, Multi-Class Classification, Category Hierarchy, Feature Fusion, Image Classification
