Active Learning Classifier for Streaming Data

被引:4
|
作者
Wozniak, Michal [1 ]
Cyganek, Boguslaw [2 ,3 ]
Kasprzak, Andrzej [1 ]
Ksieniewicz, Pawel [1 ]
Walkowiak, Krzysztof [1 ]
机构
[1] Wroclaw Univ Technol, Fac Elect, Dept Syst & Comp Networks, Wybreze Wyspianskiego 27, PL-50370 Wroclaw, Poland
[2] Wroclaw Univ Technol, ENGINE Ctr, Wybreze Wyspianskiego 27, PL-50370 Wroclaw, Poland
[3] AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland
来源
关键词
Pattern classification; Data stream classification; Active learning;
D O I
10.1007/978-3-319-32034-2_16
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This work reports the research on active learning approach applied to the data stream classification. The chosen characteristics of the proposed frameworks were evaluated on the basis of the wide range of computer experiments carried out on the three benchmark data streams. Obtained results confirmed the usability of proposed method to the data stream classification with the presence of incremental concept drift.
引用
收藏
页码:186 / 197
页数:12
相关论文
共 50 条
  • [1] Active Learning with Evolving Streaming Data
    Zliobaite, Indre
    Bifet, Albert
    Pfahringer, Bernhard
    Holmes, Geoff
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT III, 2011, 6913 : 597 - 612
  • [2] Active Learning With Drifting Streaming Data
    Zliobaite, Indre
    Bifet, Albert
    Pfahringer, Bernhard
    Holmes, Geoffrey
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (01) : 27 - 39
  • [3] Active Learning Classification of Drifted Streaming Data
    Wozniak, Michal
    Ksieniewicz, Pawel
    Cyganek, Boguslaw
    Kasprzak, Andrzej
    Walkowiak, Krzysztof
    INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE 2016 (ICCS 2016), 2016, 80 : 1724 - 1733
  • [4] Active Learning for Streaming Data in A Contextual Bandit Framework
    Song, Linqi
    Xu, Jie
    Li, Congduan
    ICCDE 2019: PROCEEDINGS OF THE 2019 5TH INTERNATIONAL CONFERENCE ON COMPUTING AND DATA ENGINEERING, 2019, : 29 - 35
  • [5] Active Learning with an Adaptive Classifier for Inaccessible Big Data Analysis
    Jahan, Sadia
    Islam, Md Rafiqul
    Hasib, Khan Md
    Naseem, Usman
    Islam, Md Saiful
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [6] Active Learning for Accurate Analysis of Streaming Partial Discharge Data
    Hai-Long Nguyen
    Gomes, Joao Bartolo
    Wu, Min
    Cao, Hong
    Cao, Jianneng
    Krishnaswamy, Shonali
    2015 IEEE CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (PHM), 2015,
  • [7] Confidence Decision Trees via Online and Active Learning for Streaming Data
    De Rosa, Rocco
    Cesa-Bianchi, Nicolo
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2017, 60 : 1031 - 1055
  • [8] Properties of a GP Active Learning Framework for Streaming Data with Class Imbalance
    Khanchi, Sara
    Heywood, Malcolm I.
    Zincir-Heywood, A. Nur
    PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17), 2017, : 945 - 952
  • [9] SETL: a transfer learning based dynamic ensemble classifier for concept drift detection in streaming data
    Arora, Shruti
    Rani, Rinkle
    Saxena, Nitin
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (03): : 3417 - 3432
  • [10] Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis
    Prasad, Gollanapalli, V
    Rao, S. Krishna Mohan
    Sharma, Kapil
    Venkatadri, M.
    Krishna, B. Rama
    INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2022, 13 (05) : 349 - 356