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| Fuzzy Rough Set Model Based on Label Relations |
| GUO Rongchao, LI Deyu, WANG Suge |
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 |
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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.
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Received: 06 May 2017
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| Fund:Supported by National Natural Science Foundation of China(No.61672331,61632011,61573231,61432011,U1435212) |
About author:: GUO Rongchao, born in 1993, master student. Her research interests include rough sets and multi-label learning.)
(LI Deyu(Corresponding author), born in 1965, Ph.D., professor. His research inte-rests include granular computing and machine learning.)
(WANG Suge, born in 1964, Ph.D., professor. Her research interests include natural language processing and text mining.) |
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[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. |
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2017, Vol. 30 
