Progressive interactive training: A sequential neural network ensemble learning method

This paper introduces a progressive interactive training scheme (PITS) for neural network (NN) ensembles. The scheme trains NNs in an ensemble one by one in a sequential fashion where the outputs of all previously trained NNs are stored and updated in a common location, called information center (IC). The communication among NNs is maintained indirectly through IC, reducing interaction among NNs. In this study, PITS is formulated as a derivative of simultaneous interactive training, negative correlation learning. The effectiveness of PITS is evaluated on a suite of 20 benchmark classification problems. The experimental results show that the proposed training scheme can improve the performance of ensembles. Furthermore, the PITS is incorporated with two very popular ensemble training methods, bagging and boosting. It is found that the performance of bagging and boosting algorithms can be improved by incorporating PITS with their training processes.