Parallel weak learners, a novel ensemble method

Ensemble methods have proved to be an effective tool to increase the performance of pattern recognition applications. An ensemble method behaves like an expert committee in predicting the class to which a sample belongs. In this paper, we present a novel ensemble method with high classification accuracy and resistance to noisy data. In our proposed method, we exploit a type of bagging in which the bagging process is carried out on attributes instead of data samples. By testing this method on two well-known databases, we show that the proposed ensemble method is comparable in accuracy and efficiency to the state-of-the-art classifiers.