ISSN 0253-2778

CN 34-1054/N

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A calibrated lable ranking method based on naive Bayes

  • Chongqing Key Laboratory of Computational Intelligence ,Chongqing University of Posts and Telecommunications ,Chongqing 400065 ,China)

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https://doi.org/10.3969/j.issn.0253-2778.2018.01.009
  • Received Date: 22 May 2017
  • Rev Recd Date: 23 June 2017
  • Publish Date: 31 January 2018
    Abstract

  • Abstract

    The traditional calibrated label ranking algorithm (calibrated label ranking, CLR) uses pairs of label associations to transform and predict results. Its algorithmic calibration is achievely comparing it with the basis of binary relevance (BR). Its prediction has a certain dependence on the results of BR, thus incurring some limitations on the prediction of some datasets. To better distinguish between the relevance and irrelevance of the label, a method is presented for calibrating label boundary regions, which further corrects the boundary portion of the relevant label and the irrelevant label using Bayesian probability, thereby improving the accuracy of the classification of the boundary domain. CLR method based on naive Bayes(NBCLRM) presented is compared with seven traditional methods such as calibrated label ranking. Experimental results show that the proposed algorithm can not only adjust prediction results by modifying the thresholds ε and μ, but also effectively improve the performance of traditional multi-label learning methods.

    Abstract

    The traditional calibrated label ranking algorithm (calibrated label ranking, CLR) uses pairs of label associations to transform and predict results. Its algorithmic calibration is achievely comparing it with the basis of binary relevance (BR). Its prediction has a certain dependence on the results of BR, thus incurring some limitations on the prediction of some datasets. To better distinguish between the relevance and irrelevance of the label, a method is presented for calibrating label boundary regions, which further corrects the boundary portion of the relevant label and the irrelevant label using Bayesian probability, thereby improving the accuracy of the classification of the boundary domain. CLR method based on naive Bayes(NBCLRM) presented is compared with seven traditional methods such as calibrated label ranking. Experimental results show that the proposed algorithm can not only adjust prediction results by modifying the thresholds ε and μ, but also effectively improve the performance of traditional multi-label learning methods.
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    • References(17)
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    WANG J, HUANG P L, SUN K W, et al. Ensemble of cost-sensitive hypernetworks for class-imbalance learning[C]// Proceedings of the International Conference on Systems, Man, and Cybernetics. Manchester, UK: IEEE Press, 2013: 1883-1888.
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    TSOUMAKAS G, VLAHAVAS I. Random k-Labelsets: An Ensemble Method for Multilabel Classification[M]// Machine Learning: ECML 2007. Springer, 2007:A122.
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    READ J, PFAHRINGER B, HOLMES G, et al. Classifier chains for multi-label classification[J]. Machine Learning, 2011, 85(3): 333-359.
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    TSOUMAKAS G, SPYROMITROS-XIOUFIS E, VILCEK J, et al. MULAN: A Java library for multi-label learning[J]. Journal of Machine Learning Research, 2011, 12(7): 2411-2414.
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    [1]
    王小妮. 数据挖掘技术[M]. 1版. 北京: 北京航空航天大学出版社, 2014.
    [2]
    ZHANG M L, ZHOU Z H. A review on multi-label learning algorithms[J]. Knowledge & Data Engineering IEEE Transactions on, 2014, 26(8): 1819-1837.
    [3]
    李思男, 李宁, 李战怀. 多标签数据挖掘技术:研究综述[J]. 计算机科学, 2013, 40(4): 14-21.
    LI Sinan, LI Ning, LI Zhanhuai. Multi-label data mining: A survey[J]. Computer Science, 2013, 40(4): 14-21.
    [4]
    ANCULEF R, FLAOUNAS I, CRISTIANINI N. Efficient classification of multi-labeled text streams by clashing[J]. Expert Systems with Applications, 2016, 41(11): 5431-5450.
    [5]
    YU Y, PEDRYCZ W, MIAO D Q. Neighborhood rough sets based multi-label classification for automatic image annotation[J]. International Journal of Approximate Reasoning, 2013, 54(9):1373-1387.
    [6]
    LO H Y, WANG J C, WANG H M, et al. Cost-sensitive multi-label learning for audio tag annotation and retrieval[J]. IEEE Transactions on Multimedia, 2011, 13(3): 518-529.
    [7]
    YU G X, RANGWALA H, DOMENICONI C, et al. Protein function prediction using multilabel ensemble classification[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2013, 10(4):1045-1057.
    [8]
    YU G X, RANGWALA H, DOMENICONI C, et al. Protein function prediction with incomplete annotations[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2014, 11(3):579-591.
    [9]
    TAHA A Y, TIUN S. Binary relevance (BR) method classifier of multi-label classification for arabic text[J]. Journal of Theoretical and Applied Information Technology, 2016, 84(3): 414-422.
    [10]
    FRNKRANZ J, HLLERMEIER E, MENCíA E L, et al. Multilabel classification via calibrated label ranking[J]. Machine Learning, 2008, 73(2): 133-153.
    [11]
    WANG J, HUANG P L, SUN K W, et al. Ensemble of cost-sensitive hypernetworks for class-imbalance learning[C]// Proceedings of the International Conference on Systems, Man, and Cybernetics. Manchester, UK: IEEE Press, 2013: 1883-1888.
    [12]
    TSOUMAKAS G, VLAHAVAS I. Random k-Labelsets: An Ensemble Method for Multilabel Classification[M]// Machine Learning: ECML 2007. Springer, 2007:A122.
    [13]
    READ J, PFAHRINGER B, HOLMES G, et al. Classifier chains for multi-label classification[J]. Machine Learning, 2011, 85(3): 333-359.
    [14]
    TSOUMAKAS G, SPYROMITROS-XIOUFIS E, VILCEK J, et al. MULAN: A Java library for multi-label learning[J]. Journal of Machine Learning Research, 2011, 12(7): 2411-2414.
    [15]
    Mulan: A Java Library for Multi-Label Learning[DB/OL]. [2017-05-06]http://mulan.sourceforge.net/datasets-mlc.html.
    [16]
    HE Z F, YANG M, LIU H D. Joint learning of multi-label classification and label correlations[J]. Journal of Software, 2014, 25(9): 1967-1981.
    [17]
    周志华. 机器学习[M].北京: 清华大学出版社, 2016.
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