Coupled Attribute Similarity Learning on Categorical Data for Multi-Label Classification  

作　　者：Zhenwu Wang Longbing Cao 

机构地区：[1]Department of Computer Science and Technology,China University of Mining and Technology(Beijing),Beijing 100083,China [2]The Advanced Analytics Institute University of Technology,Sydney 2007,Australia

出　　处：《Journal of Beijing Institute of Technology》2017年第3期404-410,共7页北京理工大学学报（英文版）

基　　金：Supported by Australian Research Council Discovery(DP130102691);the National Science Foundation of China(61302157);China National 863 Project(2012AA12A308);China Pre-research Project of Nuclear Industry(FZ1402-08)

摘　　要：In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain,which can be regarded as the important degree of each attribute in the attribute learning method,but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels.The paper compared the CASonMLCD method with the OF distance and Jaccard similarity,which is based on the MLKNN algorithm according to 5common evaluation criteria.The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively,it can obtain better performance than compared methods.In this paper a novel coupled attribute similarity learning method is proposed with the basis on the multi-label categorical data（CASonMLCD）.The CASonMLCD method not only computes the correlations between different attributes and multi-label sets using information gain,which can be regarded as the important degree of each attribute in the attribute learning method,but also further analyzes the intra-coupled and inter-coupled interactions between an attribute value pair for different attributes and multiple labels.The paper compared the CASonMLCD method with the OF distance and Jaccard similarity,which is based on the MLKNN algorithm according to 5common evaluation criteria.The experiment results demonstrated that the CASonMLCD method can mine the similarity relationship more accurately and comprehensively,it can obtain better performance than compared methods.

关 键 词：COUPLED SIMILARITY MULTI-LABEL categorical data CORRELATIONS 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]