Framework of Classification Based on Multi-Value Decomposition and Multi-Label Learning
Liang Zhong Shen · Computer Systems and Applications · 2010
Classification of multi-valued and multi-labeled data is about a sample which is not only associated with a set of labels,but also with several values that include some attributes.This paper proposes a multi-valued and multi-labeled learning framework that combines multi-value decomposition with multi-label learning(MDML),using four strategies to deal with multi-valued attributes and three classical,multi-label algorithms to learn.Experimental results demonstrate that MDML significantly outperforms the decision tree based method.Meanwhile,combined methods can be applied to various types of datasets.