Shapelet Based Two-Step Time Series Positive and Unlabeled Learning  

作　　者：张翰博 王鹏 张明明 汪卫 Han-Bo Zhang;Peng Wang;Ming-Ming Zhang;Wei Wang(School of Computer Science,Fudan University,Shanghai 200438,China)

机构地区：[1]School of Computer Science,Fudan University,Shanghai 200438,China

出　　处：《Journal of Computer Science & Technology》2023年第6期1387-1402,共16页计算机科学技术学报（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant No.2020YFB1710001.

摘　　要：In the last decade,there has been significant progress in time series classification.However,in real-world in-dustrial settings,it is expensive and difficult to obtain high-quality labeled data.Therefore,the positive and unlabeled learning(PU-learning)problem has become more and more popular recently.The current PU-learning approaches of the time series data suffer from low accuracy due to the lack of negative labeled time series.In this paper,we propose a novel shapelet based two-step(2STEP)PU-learning approach.In the first step,we generate shapelet features based on the posi-tive time series,which are used to select a set of negative examples.In the second step,based on both positive and nega-tive time series,we select the final features and build the classification model.The experimental results show that our 2STEP approach can improve the average F1 score on 15 datasets by 9.1%compared with the baselines,and achieves the highest F1 score on 10 out of 15 time series datasets.

关 键 词：positive unlabeled learning time series shapelet 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]