Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Ensemble learning via negative correlation
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Knowledge Engineering Review
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Generate different neural networks by negative correlation learning
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Training regression ensembles by sequential target correction and resampling
Information Sciences: an International Journal
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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.