一种双优选的半监督回归算法

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Title:

A dual-optimal semi-supervised regression algorithm

作者:

程康明¹, 熊伟丽^1,2

1. 江南大学 物联网工程学院, 江苏 无锡 214122;
2. 江南大学 轻工过程先进控制教育部重点实验室, 江苏 无锡 214122

Author(s):

CHENG Kangming¹, XIONG Weili^1,2

1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

关键词:

无标签样本; 优选; 半监督回归; 样本密集区中心; 相似度; 高斯过程回归; 辅学习器; 主学习器; 脱丁烷塔过程; 预测性能

Keywords:

unlabeled samples; select; semi-supervised regression; center of sample dense area; similarity; Gaussian process regression; auxiliary learner; main learner; debutanizer process; prediction performance

分类号:

TP274

DOI:

10.11992/tis.201805010

摘要:

针对一些工业过程中存在的有标签样本少，而传统的半监督学习无法保证对无标签样本准确预测的问题，提出一种双优选的半监督回归算法。首先，确定有标签样本密集区中心，并计算无标签样本与该中心的相似度，实现对无标签样本的优选，同时根据有标签样本间相似度优选有标签样本；然后，利用高斯过程回归方法对选出的有标签样本建立辅学习器，以对优选出的无标签样本预测标签；最后，利用这些伪标签样本提升主学习器的预测效果。通过数值例子以及实际脱丁烷塔过程数据进行建模仿真，证明了所提方法在有标签样本较少的情况下有良好的预测性能。

Abstract:

Aiming at the problem that there are few label samples in some industrial processes and that the traditional semi-supervised learning cannot guarantee the accurate prediction of unlabeled samples, a dual-optimal semi-supervised regression algorithm is proposed in this paper. First, in this method, the center of the label-concentrated area is found, and the similarity between unlabeled samples and the center is calculated, and therefore, the unlabeled samples are optimized. At the same time, the labeled samples are selected according to similarity between the unlabeled samples and the center of the dense area. Second, by employing the Gaussian process regression method, an auxiliary learner is established according to the selected labeled sample, and then the labels of the selected unlabeled samples are predicted by the auxiliary learner. Finally, the performance of the main learner is improved with these pseudo-label samples. Through a simulation of the numerical case and the actual debutanizer process, the proposed method is verified to have a good prediction performance when the labeled samples are few.