Multi–granulation ensemble classification for incomplete data

Abstract

A new learning algorithm is introduced that can deal with incomplete data. The algorithm uses a multi-granulation ensemble of classifiers approach. Firstly, the missing attributes tree (MAT) was constructed according to the missing values of samples. Secondly, the incomplete dataset was projected into a group of data subsets based on MAT, those data subsets were used as the training sets for the neural network. Based on bagging algorithm, each data subset was used to generate a group of classifiers and then using classifier ensemble to get the final prediction on each data subset. Finally, we adopt the conditional entropy as the weighting parameter to overcome the precision insufficiency of dimension based algorithm. Numerical experiments show that our learning algorithm can reduce the influence of missing attributes for classification results, and it is superior in performance to algorithm compared.