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  • Multi-label classification via incremental clustering on an evolving data stream

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    Embargoed until: 2021-06-03
    File version
    Accepted Manuscript (AM)
    Author(s)
    Tien, Thanh Nguyen
    Manh, Truong Dang
    Anh, Vu Luong
    Liew, Alan Wee-Chung
    Liang, Tiancai
    McCall, John
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2019
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    Abstract
    With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates ...
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    With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.
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    Journal Title
    Pattern Recognition
    Volume
    95
    DOI
    https://doi.org/10.1016/j.patcog.2019.06.001
    Copyright Statement
    © 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial Intelligence and Image Processing
    Information Systems
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/386259
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    • Journal articles

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