基于PU学习和自主训练的时间序列分类模型  

Time series classification model based on PU learning and self-training

作　　者：郭芷榕 王会青[1] 白莹莹[1] GUO Zhi-rong;WANG Hui-qing;BAI Ying-ying(College of Computer Science and Technology,Taiyuan University of Technology,Taiyuan 030600,China)

机构地区：[1]太原理工大学计算机科学与技术学院,山西太原030600

出　　处：《计算机工程与设计》2018年第9期2780-2786,共7页Computer Engineering and Design

基　　金：山西省科技攻关基金项目(201603D221037-2)

摘　　要：通过分析PU学习(positive unlabeled learning)的数据分布情况和自主训练算法的迭代过程,针对时间序列监督学习中自主训练算法的过早停止问题,提出基于PU学习和改进的自主训练的时间序列分类模型。针对不同的数据分布,进行不同轮次的迭代标记,将所有未标记数据进行标记,有效避免过早停止,增强模型的泛化能力。实验结果表明,该模型在PU学习时间序列分类中,具有较高的分类准确度、分类查全率和分类F1度量值。After analyzing the data distribution of positive unlabeled learning and the iterative process of self-training algorithm,based on PU learning and improved self-training,a time series classification model was proposed.To prevent the premature stop problem and to improve the generalization capability,for different data distribution,different rounds of iterative mark were made through the model,and all the unlabeled data were marked.Experimental results indicate that the proposed model has high classification accuracy,classification rate and classification F1 metric value in PU learning time series classification.

关 键 词：时间序列 半监督学习 正例和未标记数据学习 自主训练 停止标准 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]