VoB predictors: Voting on bagging classifications

Bagging predictors relies on bootstrap sampling to maintain a set of diverse base classifiers constituting the classifier ensemble, where the diversity among base classifiers is ensured through a random sampling (with replacement) process on the original data. In this paper, we propose a random missing value corruption based bootstrap sampling process, where the objective is to enhance the diversity of the learning sets through random missing value injection, such that base classifiers can form an accurate classifier ensemble. Our VoB (voting on bagging classifications) predictors first generate multiple incomplete datasets from a base complete dataset by randomly injecting missing values with a small missing ratio, then apply a bagging predictor trained on each of the incomplete dataset to give classifications. The final prediction of a class is the result of voting on the classifications. Our empirical results show that VoB predictors significantly improve the classification performance on complete data, and perform better than bagging predictors.