Ordered Code-based Kernel Extreme Learning Machine for Ordinal Regression [基于有序编码的核极限学习顺序回归模型]

摘要

Ordinal regression is one of the supervised learning issues, which resides between classification and regression in machine learning fields. There exist many real problems in practice, which can be modeled as ordinal regression problems due to the ordering information between labels. Therefore ordinal regression has received increasing interest by many researchers recently. The Extreme Learning Machine (ELM)-based algorithms are easy to train without iterative algorithm and they can avoid the local optimal solution; meanwhile they reduce the training time compared with other learning algorithms. However, the ELM-based algorithms which are applied to ordinal regression have not been exploited much. This paper proposes a new ordered code-based kernel extreme learning ordinal regression machine to fill this gap, which combines the kernel ELM and error correcting output codes effectively. The model overcomes the problems of how to get high quality feature mappings in ordinal regression and how to avoid setting the number of hidden nodes by manual. To validate the effectiveness of this model, numerous experiments are conducted on a lot of datasets. The experimental results show that the model can improve the accuracy by 10.8% on average compared with traditional ELM-based algorithms and achieve the state- of-the-art performance with the least time.

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