A cluster based prototype reduction for online classification

Kemilly Dearo Garcia, André C.P.L.F. de Carvalho, Joao Mendes-Moreira

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    4 Citations (Scopus)
    26 Downloads (Pure)


    Data stream is a challenging research topic in which data can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, for example, a concept drift. A concept drift occurs when the concepts associated with a dataset change when new data arrive. This paper proposes a new method based on k-Nearest Neighbors that implements a sliding window requiring less instances stored for training than existing methods. For such, a clustering approach is used to summarize data by placing labeled instances considered similar in the same cluster. Besides, instances close to the uncertainty border of existing classes are also stored, in a sliding window, to adapt the model to concept drift. The proposed method is experimentally compared with state-of-the-art classifiers from the data stream literature, regarding accuracy and processing time. According to the experimental results, the proposed method has better accuracy and less time consumption when fewer information about the concepts are stored in a single sliding window.
    Original languageEnglish
    Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2018
    Subtitle of host publication19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I
    EditorsHujun Yin, David Camacho, Paulo Novais, Antonio J. Tallón-Ballesteros
    Place of PublicationCham
    Number of pages8
    ISBN (Electronic)978-3-030-03493-1
    ISBN (Print)978-3-030-03492-4
    Publication statusPublished - 9 Nov 2018
    Event19th International Conference on Intelligent Data Engineering and Automated Learning 2018 - Autonomous University of Madrid, Madrid, Spain
    Duration: 21 Nov 201823 Nov 2018
    Conference number: 19

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference19th International Conference on Intelligent Data Engineering and Automated Learning 2018
    Abbreviated titleIDEAL 2018
    Internet address


    • kNN prototyping
    • Data stream
    • Online clustering


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