Locally ordinal classifier chains for multi-label learning
Jing Qiu · Jisuanji yingyong yanjiu · 2013
The correlation among different labels plays an important role in classification problems,and recent studies have taken into account label correlation during multi-label learning.The label information is marked into the attribute space through the classifier chains and provides useful information for the other labels during the classification process.The classification results are indeterminate and instable because of the random classifier order in the classifier chain.Besides,it may cause to propagate the error label information.This paper fully considerd the local distribution of instance labels,and proposed a locally ordinal classifier chain algorithm.Experimental results show that,the new algorithm outperforms the other commonly used multi-label algorithms most of the time.