Classification of concept drift data streams

被引:0
|
作者
Padmalatha, E. [1 ]
Reddy, C. R. K. [2 ]
Rani, B. Padmaja [3 ]
机构
[1] JNTUH, Hyderabad, Andhra Pradesh, India
[2] CBIT, CSE Dept, Hyderabad, Andhra Pradesh, India
[3] JNTUH, CSE Dept, Hyderabad, Andhra Pradesh, India
关键词
data stream; ensemble; class label; concept drift;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Concept drift has been a very important concept in the realm of data streams. Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. Concept drift occurs when a set of examples has legitimate class labels at one time and has different legitimate labels at another time. This paper provides a comprehensive overview of existing concept -evolution in concept drifting techniques along different dimensions and it provides lucid vision about the ensemble's behavior when dealing with concept drifts.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] On learning guarantees to unsupervised concept drift detection on data streams
    de Mello, Rodrigo F.
    Vaz, Yule
    Grossi, Carlos H.
    Bifet, Albert
    EXPERT SYSTEMS WITH APPLICATIONS, 2019, 117 : 90 - 102
  • [32] Nacre: Proactive Recurrent Concept Drift Detection in Data Streams
    Wu, Ocean
    Koh, Yun Sing
    Dobbie, Gillian
    Lacombe, Thomas
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [33] Discussion and review on evolving data streams and concept drift adapting
    Khamassi, Imen
    Sayed-Mouchaweh, Moamar
    Hammami, Moez
    Ghedira, Khaled
    EVOLVING SYSTEMS, 2018, 9 (01) : 1 - 23
  • [34] Batch Weighted Ensemble for Mining Data Streams with Concept Drift
    Deckert, Magdalena
    FOUNDATIONS OF INTELLIGENT SYSTEMS, 2011, 6804 : 290 - 299
  • [35] A Novel Online Ensemble Approach for Concept Drift in Data Streams
    Sidhu, Parneeta
    Bhatia, M. P. S.
    Bindal, Aditya
    2013 IEEE SECOND INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP), 2013, : 550 - 555
  • [36] Detecting concept drift in data streams using model explanation
    Demsar, Jaka
    Bosnic, Zoran
    EXPERT SYSTEMS WITH APPLICATIONS, 2018, 92 : 546 - 559
  • [37] Predicting concept drift in data streams using metadata clustering
    Anderson, Robert
    Koh, Yun Sing
    Dobbie, Gillian
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [38] A Stable and Online Approach to Detect Concept Drift in Data Streams
    da Costa, Fausto Guzzo
    de Mello, Rodrigo Fernandes
    2014 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 2014, : 330 - 335
  • [39] Detecting group concept drift from multiple data streams
    Yu, Hang
    Liu, Weixu
    Lu, Jie
    Wen, Yimin
    Luo, Xiangfeng
    Zhang, Guangquan
    PATTERN RECOGNITION, 2023, 134
  • [40] Online Clustering for Novelty Detection and Concept Drift in Data Streams
    Garcia, Kemilly Dearo
    Poel, Mannes
    Kok, Joost N.
    de Carvalho, Andre C. P. L. F.
    PROGRESS IN ARTIFICIAL INTELLIGENCE, PT II, 2019, 11805 : 448 - 459