Semi-supervised Data Stream Ensemble Classifiers Algorithm Based on Cluster Assumption

摘要

Semi-supervised data stream ensemble classifiers algorithm based on cluster assumption was proposed. Although traditional semi-supervised classification algorithm can solve incomplete label data sets classification problem, but it is an unsolved problem that how to use it in data stream environment and how to improve semi-supervised classification algorithm accuracy by using data stream characters. According to analyzing generalization of semi-supervised classifier based on cluster assumption, it indicates that increasing labeled data during training moment can improve semi-supervised classifier accuracy. Making use of this conclusion, a semi-supervised data stream ensemble classifiers algorithm based on cluster assumption was proposed.