Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces
YAO Erliang1, LI Deyu1,2, LI Yanhong1, BAI Hexiang1, ZHANG Chao2
1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006
2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
The existing multi-label feature selection algorithms based on fuzzy rough sets characterize the ability of distinguishing attributes from single sample space, while the ability of attributes distinguishing labels is ignored. Therefore, a multi-label feature selection algorithm based on fuzzy discernibility relations in double spaces is proposed. Firstly, two multi-label attribute measures based on fuzzy discernibility relations are defined from the perspective of samples and labels respectively. Then, two different measures are combined by introducing weights. Finally, a multi-label feature selection algorithm is constructed based on the combined measures by utilizing the forward greedy algorithm. Results of comparative experiments on five multi-label datasets verify the effectiveness of the proposed algorithm.
姚二亮, 李德玉, 李艳红, 白鹤翔, 张超. 基于双空间模糊辨识关系的多标记特征选择[J]. 模式识别与人工智能, 2019, 32(8): 709-717.
YAO Erliang, LI Deyu, LI Yanhong, BAI Hexiang, ZHANG Chao. Multi-label Feature Selection Based on Fuzzy Discernibility Relations in Double Spaces. , 2019, 32(8): 709-717.
[1] LEE S J, JIANG J Y. Multilabel Text Categorization Based on Fuzzy Relevance Clustering. IEEE Transactions on Fuzzy Systems, 2014, 22(6): 1457-1471.
[2] WU Q Y, TAN M K, SONG H J, et al. ML-FOREST: A Multi-label Tree Ensemble Method for Multi-label Classification. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(10): 2665-2680.
[3] ZHANG M L, ZHOU Z H. A Review on Multi-label Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.
[4] LI J D, LIU H. Challenges of Feature Selection for Big Data Analy-tics. IEEE Intelligent Systems, 2017, 32(2): 9-15.
[5] HUANG J, LI G R, Huang Q M, et al. Joint Feature Selection and Classification for Multilabel Learning. IEEE Transactions on Cybernetics, 2018, 48(3): 876-889.
[6] LEE J, KIM D W. Feature Selection for Multi-label Classification Using Multivariate Mutual Information. Pattern Recognition Letters, 2013, 34(3): 349-357.
[7] LIN Y J, HU Q H, LIU J H, et al. Multi-label Feature Selection Based on Max-Dependency and Min-Redundancy. Neurocomputing, 2015, 168: 92-103.
[8] LI F, MIAO D Q, PEDRVCZ W. Granular Multi-label Feature Selection Based on Mutual Information. Pattern Recognition, 2017, 67: 410-423.
[9] ZHANG M L, PEA J M, ROBLES V. Feature Selection for Multi-label Naive Bayes Classification. Information Sciences, 2009, 179(19): 3218-3229.
[10] ZHANG M L, WU L. LIFT: Multi-label Learning with Label-Specific Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107-120.
[11] HU Q H, YU D R, LIU J F, et al. Neighborhood Rough Set Based Heterogeneous Feature Subset Selection. Information Sciences, 2008, 178(18): 3577-3594.
[12] JENSEN R, SHEN Q. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, 2007, 15(1): 73-89.
[13] ZHANG C, LI D Y, LIANG J Y. Hesitant Fuzzy Linguistic Rough Set over Two Universes Model and Its Applications. International Journal of Machine Learning and Cybernetics, 2018, 9(4): 577-588.
[14] 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.)
[15] 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.
[16] CHEN D G, ZHANG L, ZHAO S Y, et al. A Novel Algorithm for Finding Reducts with Fuzzy Rough Sets. IEEE Transactions on Fuzzy Systems, 2012, 20(2): 385-389.
[17] DAI J H, HU H, WU W Z, et al. Maximal Discernibility-Pairs-Based Approach to Attribute Reduction in Fuzzy Rough Sets. IEEE Transactions on Fuzzy Systems, 2018, 26(4): 2174-2187.
[18] ZHANG L J, HU Q H, DUAN J, et al. Multi-label Feature Selection with Fuzzy Rough Sets // Proc of the International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2014: 121-128.
[19] 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.)
[20] 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.
[21] 郭荣超,李德玉,王素格.基于标记关系的模糊粗糙集模型.模式识别与人工智能, 2017, 30(10): 952-960.
(GUO R C, LI D Y, WANG S G. Fuzzy Rough Set Model Based on Label Relations. Pattern Recognition and Artificial Intelligence, 2017, 30(10): 952-960.)
[22] LIN Y J, LI Y W, WANG C X, et al. Attribute Reduction for Multi-label Learning with Fuzzy Rough Set. Knowledge-Based Systems, 2018, 152: 51-61.
[23] ZHANG M L, ZHOU Z H. ML-KNN: A Lazy Learning Approach to Multi-label Learning. Pattern Recognition, 2007, 40(7): 2038-2048.
[24] HU X H, CERCONE N. Learning in Relational Databases: A Rough Set Approach. Computational Intelligence, 1995, 11(2): 323-338.
2019, Vol. 32 
