Transductive learning method applied to ensemble classification over data stream
适于数据流组合分类的直推学习方法

The existing strategy of combining decisions for ensemble classification method requires common labeled training samples across these ensemble classifiers. To resolve combining classifiers decisions among ensemble classification over data streams without labeled examples, a transductive constraint-based learning strategy was proposed. It satisfied the constraints measured by each local classifier based on transductive learning theory while choosing decision on test samples; thereby guaranteed the feasibility of the constraints. It solved the problems of transductive extension of maximum entropy for aggregation in distributed classification. Experimental examples prove that the proposed method can achieve higher classifying accuracy over the existing transductive approach and can be applied to ensemble classification fusing for data streams.