School of Computer and Information Technology, Shanxi University, Taiyuan 030006 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract：The data in multi-label classification tasks are usually high dimensional. Utilizing high-dimension data directly for modeling often results in a lower training efficiency or a complex model with the classifier performance reduced. For multi-label data, the concept of attribute-label matrix is proposed, a label relation based fuzzy rough set model is established, and a reduction algorithm of the model is then designed for feature selection of multi-label classification tasks. Finally, the effectiveness of the proposed method is verified on eight public datasets.
郭荣超，李德玉，王素格. 基于标记关系的模糊粗糙集模型*[J]. 模式识别与人工智能, 2017, 30(10): 952-960.
GUO Rongchao, LI Deyu, WANG Suge. Fuzzy Rough Set Model Based on Label Relations. , 2017, 30(10): 952-960.
[1] ZHU X F, LI X L, ZHANG S C. Block-Row Sparse Multiview Multilabel Learning for Image Classification. IEEE Transactions on Cybernetics, 2016, 46(2): 450-461. [2] LUO Y, LIU T L, YAO D C, et al. Multiview Matrix Completion for Multilabel Image Classification. IEEE Transactions on Image Processing, 2015, 24(8): 2355-2368. [3] CAO X C, ZHANG H, GUO X J, et al. SLED: Semantic Label Embedding Dictionary Representation for Multilabel Image Annotation. IEEE Transactions on Image Processing, 2015, 24(9): 2746-2759. [4] GONG Y C, JIA Y Q, LEUNG T, et al. Deep Convolutional Ranking for Multilabel Image Annotation[C/OL].[2017-04-21]. https://arxiv.org/pdf/1312.4894.pdf. [5] TROHIDIS K, TSOUMAKAS G, KALLIRIS G, et al. Multilabel Classification of Music into Emotions[C/OL].[2017-04-21]. https://pdfs.semanticscholar.org/0caf/7892917a53011e062c9f905 7551a601b6695.pdf. [6] LIU S M, CHEN J H. A Multi-label Classification Based Approach for Sentiment Classification. Expert Systems with Applications, 2015, 42(3): 1083-1093. [7] ZHANG Y, SU L L, YANG Z F, et al. Multi-label Emotion Tagging for Online News by Supervised Topic Model // Proc of the 17th Asia-Pacific Web Conference. Berlin, Germany: Springer, 2015: 67-79. [8] CHE Y X, JU Y, XUAN P, et al. Identification of Multi-functional Enzyme with Multi-label Classifier. Plos One, 2016, 11(4). DOI: 10.1371/journal.pone.0153503. [9] ZHU J Q, LIAO S C, YI D, et al. Multi-label CNN Based Pedestrian Attribute Learning for Soft Biometrics // Proc of the International Conference on Biometrics. Washington, USA: IEEE, 2015: 535-540. [10] XIAO X, ZOU H L, LIN W Z. iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types. The Journal of Membrane Biology, 2015, 248(4): 745-752. [11] AHMED N A, SHEHAB M A, AL-AYYOUB M, et al. Scalable Multi-label Arabic Text Classification // Proc of the 6th International Conference on Information and Communication Systems. Washington, USA: IEEE, 2015: 212-217. [12] ZHOU P, EL-GOHARY N. Ontology-Based Multilabel Text Classification of Construction Regulatory Documents. Journal of Computing in Civil Engineering, 2016, 30(4). DOI: 10.1061/(ASCE)CP.1943-5487.0000530. [13] WATANABE S. Knowing and Guessing: A Quantitative Study of Inference and Information. New York: USA: Wiley, 1969. [14] GRZYMA A-BUSSE J W. Managing Uncertainty in Expert Systems. Norwell, USA: Kluwer Academic Publishers, 1991. [15] GRZYMA A-BUSSE J W. LERS-A System for Learning from Examples Based on Rough Sets // S OWI′NSKI R, ed. Intelligent Decision Support. Berlin, Germany: Springer, 1992: 3-18. [16] HU X H, CERCONE N. Learning in Relational Databases: A Rough Set Approach. Computational Intelligence, 1995, 11(2): 323-338. [17] LI H, LI D Y, ZHAI Y H, et al. A Novel Attribute Reduction Approach for Multi-label Data Based on Rough Set Theory. Information Sciences, 2016, 367: 827-847. [18] 段 洁,胡清华,张灵均,等.基于邻域粗糙集的多标记分类特征选择算法.计算机研究与发展, 2015, 52(1): 56-65. (DUAN J, HU Q H, ZHANG L J, et al. Feature Selection for Multi-label Classification Based on Neighborhood Rough Sets. Journal of Computer Research and Development, 2015, 52(1): 56-65.) [19] DUBOIS D, PRADE H. Rough Fuzzy Sets and Fuzzy Rough Sets. International Journal of General System, 1990, 17(2/3): 191-209. [20] 张 超,李德玉.勾股模糊粗糙集及其在多属性决策中的应用.小型微型计算机系统, 2016, 37(7): 1531-1535. (ZHANG C, LI D Y. Pythagorean Fuzzy Rough Sets and Its Application in Multiple Attribute Decision Making. Journal of Chinese Computer System, 2016, 37(7): 1531-1535.) [21] HU Q H, XIE Z X, YU D R. Hybrid Attribute Reduction Based on a Novel Fuzzy-Rough Model and Information Granulation. Pattern Recognition, 2007, 40(12): 3509-3521. [22] JENSEN R, SHEN Q. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, 2007, 15(1): 73-89. [23] JENSEN R, SHEN Q. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(12): 1457-1471. [24] ZHANG L J, HU Q H, ZHOU Y C, et al. Multi-label Attribute Evaluation Based on Fuzzy Rough Sets // Proc of the International Conference on Rough Sets and Current Trends in Computing. Berlin, Germany: Springer, 2014: 100-108. [25] SHEN X P, BOUTELL M, LUO J B, et al. Multilabel Machine Learning and Its Application to Semantic Scene Classification. Proc of the SPIE, 2003, 5307: 188-199. [26] WANG C Z, QI Y L, SHAO M W, et al. A Fitting Model for Feature Selection with Fuzzy Rough Sets. IEEE Transactions on Fuzzy Systems, 2017, 25(4): 741-753. [27] 陈德刚.模糊粗糙集理论与方法.北京:科学出版社, 2013. (CHEN D G. Fuzzy Rough Sets Theory and Method. Beijing, China: Science Press, 2013.)
[28] TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Mining Multi-label Data // MAIMON O, ROKACH L, eds. Data Mining and Knowledge Discovery Handbook. Berlin, Germany: Springer, 2009: 667-685.
2017, Vol. 30 
