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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Negative Correlation Ensemble Learning for Ordinal Regression
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Negative Correlation Ensemble Learning for Ordinal Regression

机译：负相关集合学习的序数回归

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摘要

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.
机译：本文针对序数回归问题提出了两种神经网络阈值集成模型。对于第一种集成方法，阈值是先验固定的，在训练过程中不会更改。第二个将集合中每个成员的阈值视为自由参数，从而可以在训练过程中对其进行修改。这是通过重新设置这些可调阈值来实现的，从而避免了它们对于顺序回归问题必须满足的约束。在训练过程中，参数更新考虑了每个成员生成的不同投影中的多样性。使用鼓励多样性的误差函数以明确的方式促进这种多样性，将众所周知的负相关学习框架扩展到序数回归领域，并继承其许多优良特性。实验结果表明，该算法在考虑四个序数回归指标时可以达到竞争性的泛化性能。

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