The cascade-correlation learning architecture
Advances in neural information processing systems 2
Ensemble learning via negative correlation
Neural Networks
Machine-Learning with cellular automata
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Two bagging algorithms with coupled learners to encourage diversity
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Parallel Approach for Ensemble Learning with Locally Coupled Neural Networks
Neural Processing Letters
Local negative correlation with resampling
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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The concept of Diversity is now recognized as a key characteristic of successful ensembles of predictors. In this paper we investigate an algorithm to generate diversity locally in regression ensembles of neural networks, which is based on the idea of imposing a neighborhood relation over the set of learners. In this algorithm each predictor iteratively improves its state considering only information about the performance of the neighbors to generate a sort of local negative correlation. We will assess our technique on two real data sets and compare this with Negative Correlation Learning, an effective technique to get diverse ensembles. We will demonstrate that the local approach exhibits better or comparable results than this global one.