HE Zhifen, YANG Ming, LIU Huidong. Multi-task Joint Feature Selection for Multi-label Classification[J]. Chinese Journal of Electronics, 2015, 24(2): 281-287. doi: 10.1049/cje.2015.04.009
Citation: HE Zhifen, YANG Ming, LIU Huidong. Multi-task Joint Feature Selection for Multi-label Classification[J]. Chinese Journal of Electronics, 2015, 24(2): 281-287. doi: 10.1049/cje.2015.04.009

Multi-task Joint Feature Selection for Multi-label Classification

doi: 10.1049/cje.2015.04.009
Funds:  This work is supported by National Natural Science Foundation of China (No.61272222, No.61003116), Natural Science Foundation of Jiangsu Province of China (No.BK2011782), and Key (Major) Program of Natural Science Foundation of Jiangsu Province of China (No.BK2011005).
More Information
  • Corresponding author: YANG Ming was born in 1964. He received the Ph.D. degree from Southeast University in 2004. He is currently a professor at the school of computer science and technology of Nanjing Normal University. His research interests include data mining, pattern recognition and machine learning. (Email:m.yang@njnu.edu.cn)
  • Publish Date: 2015-04-10
  • Multi-label learning deals with each instance which may be associated with a set of class labels simultaneously. We propose a novel multi-label classification approach named MFSM (Multi-task joint feature selection for multi-label classification). In MFSM, we compute the asymmetric label correlation matrix in the label space. The multi-label learning problem can be formulated as a joint optimization problem including two regularization terms, one aims to consider the label correlations and the other is used to select the similar sparse features shared among multiple different classification tasks (each for one label). Our model can be reformulated into an equivalent smooth convex optimization problem which can be solved by the Nesterov's method. The experiments on sixteen benchmark multi-label data sets demonstrate that our method outperforms the state-of-the-art multi-label learning algorithms.
  • loading
  • R.E. Schapire and Y. Singer, “Boostexter: A boosting-based system for text categorization”, Machine Learning, Vol.39, No.2/3, pp.135-168, 2000.
    Y.Y. Jiang, P. Li and Q. Wang, “Labeled LDA model based on shared background topics”, Acta Electronic Sinica, Vol.41, No.9, pp.1794-1799, 2013. (in Chinese)
    M.R. Boutell, J. Luo, et al., “Learning multi-label scene classification”, Pattern Recognition, Vol.37, No.9, pp.1757-1771, 2004.
    C. Wang, S. Yan, et al., “Multi-label sparse coding for automatic image annotation”, IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA, pp.1643-1650, 2009.
    M.L. Zhang and Z.H. Zhou, “Multilabel neural networks with applications to functional genomics and text categorization”, IEEE Transactions on Knowledge and Data Engineering, Vol.18, No.10, pp.1338-1351, 2006.
    A. Elisseeff and J. Weston, “A kernel method for multi-labelled classification”, Proc. of the Fifteenth Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp.681-687, 2001.
    K. Trohidis, G. Tsoumakas, et al., “Multilabel classification of music into emotions”, Proc. of the 9th International Conference on Music Information Retrieval, Philadephia, PA, USA, pp.325-330, 2008.
    M.L. Zhang and Z.H. Zhou, “ML-KNN: A lazy learning approach to multi-label learning”, Pattern Recognition, Vol.40, No.7, pp.2038-2048, 2007.
    M.L. Zhang, J.M. Peña and V. Robles, “Feature selection for multi-label naive Bayes classification”, Information Sciences, Vol.179, No.19, pp.3218-3229, 2009.
    J.H. Xu, “Fast multi-label core vector machine”, Pattern Recognition, Vol.46, No.3, pp.885-898, 2013.
    J.H. Xu, “An extended one-versus-rest support vector machine for multi-label classification”, Neurocomputing, Vol.74, No.17, pp.3114-3124, 2011.
    M.L. Zhang and Z.H. Zhou, “A review on multi-label learning algorithms”, IEEE Transactions on Knowledge and Data Engineering, Vol.26, No.8, pp.1819-1837, 2014.
    S.J. Huang and Z.H. Zhou, “Multi-label learning by exploiting label correlations locally”, Proc. of the 26th AAAI Conference on Artificial Intelligence, Toronto, Canada, pp.949-955, 2012.
    S.J. Huang, Y. Yu and Z.H. Zhou, “Multi-label hypothesis reuse”, Proc. of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp.525-533, 2012.
    M.L. Zhang and K. Zhang, “Multi-label learning by exploiting label dependency”, Proc. of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C., USA, pp.999-1008, 2010.
    M.L. Zhang, “LIFT: Multi-label learning with label-specific features”, Proc. of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spanish, pp.1609-1614, 2011.
    J. Fürnkranz, E. Hüllermeier, et al., “Multilabel classification via calibrated label ranking”, Machine Learning, Vol.73, No.2, pp.133-153, 2008.
    S.W. Ji and J.P. Ye, “Linear dimensionality reduction for multilabel classification”, Proc. of the 21st International Joint Conference on Artifical Intelligence, California, USA, pp.1077-1082, 2009.
    Y. Zhang and Z.H. Zhou, “Multilabel dimensionality reduction via dependence maximization”, ACM Transactions on Knowledge Discovery from Data, Vol.4, No.3, pp.1-21, 2010.
    J. Liu, S.W. Ji and J.P. Ye, “Multi-task feature learning via efficient l2,1-norm minimization”, Proc. of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, pp.339-348, 2009.
    Y. Nesterov and I.U.E Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Kluwer Academic Publishers, Holland, 2004.
    J. Read, B. Pfahringer, G. Holmes, et al., “Classifier chains for multi-label classification”, Machine Learning, Vol.85, No.3, pp.333-359, 2011.
    G. Tsoumakas, I. Katakis and I. Vlahavas, “Random k-labelsets for multilabel classification”, IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.7, pp.1079-1089, 2011.
    E. Hüllermeier, J. Fürnkranz, et al., “Label ranking by learning pairwise preferences”, Artificial Intelligence, Vol.172, No.16, pp.1897-1916, 2008.
    R. Caruana, “Multitask learning”, Machine Learning, Vol.28, No.1, pp.41-75, 1997.
    A. Evgeniou and M. Pontil, “Multi-task feature learning”, Proc. of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, B.C., Canada, pp.41-48, 2007.
    S. Ben-David and R. Schuller, “Exploiting task relatedness for multiple task learning”, Proc. of the 16th Annual Conference on Learning Theory, Washington, D.C., USA, pp.567-580, 2003.
    L.S. Qiao, S.C. Chen and X.Y. Tan, “Sparsity preserving projections with applications to face recognition”, Pattern Recognition, Vol.43, No.1, pp.331-341, 2010.
    D.L. Donoho, “Compressed sensing”, IEEE Transactions on Information Theory, Vol.52, No.4, pp.1289-1306, 2006.
    J. Wright, A.Y. Yang, et al., “Robust face recognition via sparse representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.31, No.2, pp.210-227, 2009.
    Y.H. Guo and W. Xue, “Probabilistic multi-label classification with sparse feature learning”, Proc. of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, pp.1373-1379, 2013.
    S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, UK, 2004.
    G. Tsoumakas, E.S. Xioufis, J. Vilcek, et al., “Mulan: A java library for multi-label learning”, The Journal of Machine Learning Research, Vol.12, No.7, pp.2411-2414, 2011.
    J. Demsar, “Statistical comparisons of classifiers over multiple data sets”, The Journal of Machine Learning Research, Vol.7, pp.1-30, 2006.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (315) PDF downloads(1681) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return