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基于有序编码的核极限学习顺序回归模型

李佩佳 石勇 汪华东 牛凌峰

李佩佳, 石勇, 汪华东, 牛凌峰. 基于有序编码的核极限学习顺序回归模型[J]. 电子与信息学报, 2018, 40(6): 1287-1293. doi: 10.11999/JEIT170765
引用本文: 李佩佳, 石勇, 汪华东, 牛凌峰. 基于有序编码的核极限学习顺序回归模型[J]. 电子与信息学报, 2018, 40(6): 1287-1293. doi: 10.11999/JEIT170765
LI Peijia, SHI Yong, WANG Huadong, NIU Lingfeng. Ordered Code-based Kernel Extreme Learning Machine for Ordinal Regression[J]. Journal of Electronics and Information Technology, 2018, 40(6): 1287-1293. doi: 10.11999/JEIT170765
Citation: LI Peijia, SHI Yong, WANG Huadong, NIU Lingfeng. Ordered Code-based Kernel Extreme Learning Machine for Ordinal Regression[J]. Journal of Electronics and Information Technology, 2018, 40(6): 1287-1293. doi: 10.11999/JEIT170765

基于有序编码的核极限学习顺序回归模型

doi: 10.11999/JEIT170765
基金项目: 

国家自然科学基金(71110107026, 71331005, 91546201, 11671379, 111331012),中国科学院大学资助项目(Y55202LY00)

Ordered Code-based Kernel Extreme Learning Machine for Ordinal Regression

Funds: 

The National Natural Science Foundation of China (71110107026, 71331005, 91546201, 11671379, 111331012), The Grant of University of Chinese Academy of Sciences (Y55202LY00)

  • 摘要: 顺序回归是机器学习领域中介于分类和回归之间的有监督问题。在实际中,许多带有序关系标签的问题都可以被建模成顺序回归问题,因此顺序回归受到众多学者的关注。基于极限学习机(ELM)的算法能有效避免因迭代过程陷入的局部最优解,减少训练时间,但基于极限学习机的算法在顺序回归问题上的研究较少。该文将核极限学习机与纠错输出编码相结合,提出了一种基于有序编码的核极限学习顺序回归模型。该模型有效解决了如何在顺序回归中取得良好的特征映射以及如何避免传统极限学习机中隐层节点个数依赖于人工设置的问题。为验证提出模型的有效性,该文在多个顺序回归数据集上进行了测试,测试结果表明,相比于传统ELM模型,该文提出的模型在准确率上平均提升了10.8%,在数据集上预测表现最优,而且获得了最短的训练时间,从而验证了模型的有效性。
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    • 收稿日期:  2017-07-28
    • 修回日期:  2018-01-22
    • 刊出日期:  2018-06-19

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