Multi-valued And Multi-labeled Data Classification

With the rapid development of computer technology, internet and database system, more and more applications are combined with multi-valued and multi-labeled datasets. Hence, multi-valued and multi-labeled classification has become a hot topic for researchers in data mining and machine learning.At present, most of the existing researches are done on multi-labeled classification without consideration about multi-valued problem. Meanwhile, the correlations between different labels are not studied adequately. What is more, lack of labeled sample results in insufficient information to learn during the training stage. All these arise new challenges to traditional classifiers. There are three contributions of this thesis. Firstly, it puts forward a new learning framework for multi-valued and multi-labeled classification by combining multi-value decomposition with multi-labeled classification algorithms. Five efficient decomposition methods are proposed and Rank Order method performs the best. Secondly, based on the study of Bayesian network, this thesis constructs a multi-labeled Bayesian network with the combination of General Bayesian network and Multi-net Bayesian network. The proposed algorithm can learn the correlations of labels in a better way, enhancing the accuracy of classification largely. Thirdly, as to the lack of labeled samples, an active learning and semi-supervised multi-labeled classification algorithm is conducted alternately based on hierarchical model. Experimental results demonstrates this algorithm greatly boosts the efficiency and robust of the classifier.This thesis provides an effective way to learn correlations between different labels and to construct a robust classifier with limited number of the labeled samples. Through combining multi-valued decomposition and multi-label classification algorithms, it builds a new learning framework for multi-valued and multi-labeled datasets.