Abstract：Ensemble method is a simple and effective method to deal with incomplete data for classification. However,the weight of each sub-classifier in ensemble classification algorithm for incomplete data is mainly determined by the size and dimension of corresponding sub-dataset at present. The contributions of the missing attributes are different,and information entropy is introduced to measure these differences,thus,a novel algorithm for incomplete data named Entropy Ensemble Classification Algorithm (EECA) is proposed in this paper. The ensemble classifier with BP neural network being base classifier is applied on UCI dataset. The experimental results show that EECA determining the weight for sub-classifier by information entropy is better than the algorithm by using simple weight.
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2014, Vol. 27 
