Ensemble Learning with Correlation-Based Penalty
Yong Liu, Qiangfu Zhao, Yan Pei · 2014
Ensemble learning system could lessen the degree of overfitting that often appear in the supervised learning process for a single learning model. However, overfitting had still been observed in negative correlation learning that is an ensemble learning method with correlation-based penalty. Two constraints were introduced into negative correlation learning in order to conquer such overfitting. One is the lower bound of error rate (LBER). The other is the upper bound of error output (UBEO). With LBER and UBEO, negative correlation learning will selectively learn the data points. After the performance becomes better than LBER, those unlearned data points with the error output larger than UBEO would not be learned anymore in negative correlation learning. This paper presented the experimental results to explain how these two constraints would affect the performance of negative correlation learning.