ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (8): 1781-1791.doi: 10.7544/issn1000-1239.2016.20160223

所属专题: 2016数据挖掘前沿技术专题

• 人工智能 • 上一篇    下一篇

基于在线迁移学习的重现概念漂移数据流分类

文益民1,2,3,唐诗淇1,冯超1,高凯4   

  1. 1(桂林电子科技大学计算机与信息安全学院 广西桂林 541004); 2(广西可信软件重点实验室(桂林电子科技大学) 广西桂林 541004); 3(广西信息科学实验中心(桂林电子科技大学) 广西桂林 541004); 4(河北科技大学信息学院 石家庄 050018) (ymwen2004@aliyun.com)
  • 出版日期: 2016-08-01
  • 基金资助: 
    国家自然科学基金项目(61363029,U1501252);广西区自然科学基金项目(2014GXNSFAA118395);广西区科学研究与技术开发项目(桂科攻14124005-2-1);广西信息科学中心项目(YB408)

Online Transfer Learning for Mining Recurring Concept in Data Stream Classification

Wen Yimin1,2,3, Tang Shiqi1, Feng Chao1,Gao Kai4   

  1. 1(School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004);2(Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin, Guangxi 541004);3(Guangxi Experiment Center of Information Science (Guilin University of Electronic Technology), Guilin, Guangxi 541004);4(School of Information Science & Engineering, Hebei University of Science and Technology, Shijiazhuang 050018)
  • Online: 2016-08-01

摘要: 随着大数据时代的到来,数据流分类被应用于诸多领域,如:垃圾邮件过滤、市场预测及天气预报等.重现概念是这些应用领域的重要特点之一.针对重现概念的学习与分类问题中的“负迁移”和概念漂移检测的滞后性,提出了一种基于在线迁移学习的重现概念漂移数据流分类算法——RC-OTL.RC-OTL在检测到概念漂移时存储刚学习的一个基分类器,然后计算最近的样本与存储的各历史分类器之间的领域相似度,以选择最适合对后续样本进行学习的源分类器,从而改善从源领域到目标领域的知识迁移.另外,RC-OTL还在概念漂移检测之前根据分类准确率选择合适的分类器对后续样本分类.初步的理论分析解释了RC-OTL为什么能有效克服“负迁移”,实验结果进一步表明:RC-OTL的确能有效提高分类准确率,并且在遭遇概念漂移后能更快地适应后续样本.

关键词: 概念漂移, 迁移学习, 重现概念, 在线学习, 负迁移

Abstract: At the age of big data, data stream classification is being applied to many fields, like spam filtering, market predicting, and weather forecasting, et al, in which recurring concept is an important character. Aiming to reduce the influence of negative transfer and improve the lag of detection of concept drift, RC-OTL is proposed for mining recurring concepts in data stream based on online transfer learning strategy. When a concept drift is detected, RC-OTL selects one current base classifier to store, and then computes the domain similarities between the current training samples and the stored classifiers, in order to select the most appropriate source classifier to combine with a new classifier for learning the upcoming samples, which results in knowledge transfer from the source domain to the target domain. In addition, RC-OTL can select appropriate classifier to classify when the current classification accuracy is detected below a given threshold before concept drift detection. The preliminary theory analysis explains why RC-OTL can reduce negative transfer effectively, and the experiment results further illustrates that RC-OTL can efficiently promote the cumulate accuracy of data stream classification, and faster adapt to the samples of new concept after concept drift takes place.

Key words: concept drift, transfer learning, recurring concept, online learning, negative transfer

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