Collaborative data stream mining in ibiquitous environment using dynamic classifier selection

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.Read More:http://www.worldscientific.com/doi/abs/10.1142/S0219622013500375
Original languageUnknown
Pages (from-to)1287-1308
JournalInternational Journal Of Information Technology & Decision Making
Volume12
Issue number6
DOIs
Publication statusPublished - 1 Jan 2013

Keywords

    Cite this

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    title = "Collaborative data stream mining in ibiquitous environment using dynamic classifier selection",
    abstract = "In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.Read More:http://www.worldscientific.com/doi/abs/10.1142/S0219622013500375",
    keywords = "Collaborative data stream mining, ubiquitous knowledge discovery, performance evaluation, concept drift",
    author = "{DEE Group Author} and Sousa, {Pedro Alexandre da Costa}",
    year = "2013",
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    doi = "10.1142/S0219622013500375",
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    AU - DEE Group Author

    AU - Sousa, Pedro Alexandre da Costa

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    N2 - In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.Read More:http://www.worldscientific.com/doi/abs/10.1142/S0219622013500375

    AB - In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy.Coll-Streamintegrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.Read More:http://www.worldscientific.com/doi/abs/10.1142/S0219622013500375

    KW - Collaborative data stream mining

    KW - ubiquitous knowledge discovery

    KW - performance evaluation

    KW - concept drift

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