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Transition Learning between Balanced Ensemble Learning and Negative Correlation Learning

In this paper, transition learning was introduced between balanced ensemble learning and negative correlation learning. The idea of transition learning is to apply balanced ensemble learning for a certain time, and then to switch to negative correlation learning. The short learning period with the sudden changes of learning behaviors is called transition learning. Experimental studies had been conducted to examine the learning behaviors in the transition process. It was found that the training error rates had big sudden changes in the beginning of transition process. The changes in the training error rates became smaller and smaller at the end of transition process. The more interesting results are how such sudden change on the training set would lead to the testing set. By observing the performance on the testing error rates, it was found that transition learning were able to prevent the learning from over fitting.