Title :
Negative Correlation Ensemble Learning for Ordinal Regression
Author :
Fernandez-Navarro, Francisco ; Gutierrez, Pedro Antonio ; Hervas-Martinez, Casar ; Xin Yao
Author_Institution :
Eur. Space Res. & Technol. Centre, Eur. Space Agency, Noordwijk, Netherlands
Abstract :
In this paper, two neural network threshold ensemble models are proposed for ordinal regression problems. For the first ensemble method, the thresholds are fixed a priori and are not modified during training. The second one considers the thresholds of each member of the ensemble as free parameters, allowing their modification during the training process. This is achieved through a reformulation of these tunable thresholds, which avoids the constraints they must fulfill for the ordinal regression problem. During training, diversity exists in different projections generated by each member is taken into account for the parameter updating. This diversity is promoted in an explicit way using a diversity-encouraging error function, extending the well-known negative correlation learning framework to the area of ordinal regression, and inheriting many of its good properties. Experimental results demonstrate that the proposed algorithms can achieve competitive generalization performance when considering four ordinal regression metrics.
Keywords :
generalisation (artificial intelligence); learning (artificial intelligence); neural nets; regression analysis; competitive generalization performance; diversity-encouraging error function; first ensemble method; negative correlation ensemble learning; negative correlation learning framework; neural network threshold ensemble model; ordinal regression metrics; ordinal regression problem; parameter updating; tunable threshold reformulation; Negative correlation learning (NCL); neural network ensembles; ordinal regression; threshold methods;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2268279