Computer Science ›› 2017, Vol. 44 ›› Issue (10): 289-295, 317.doi: 10.11896/j.issn.1002-137X.2017.10.052

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Multi-label Feature Selection Algorithm Based on Label Weighting

LIN Meng-lei, LIU Jing-hua, WANG Chen-xi and LIN Yao-jin   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In multi-label learning,each sample is described as a feature vector and simultaneously associated with multiple class labels.Feature selection is able to remove irrelevant and redundant features,which is an efficient measure of overcoming the curse of dimensionality for multi-label data.Label has different separability with sample,which may provide some usefull informations for multi-label learning.Based on this assumption,a multi-label feature selection algorithm based on label weighting was proposed in this paper.First,the margin of sample in all feature space is calculated and it is used as label weighting.Then,the distinguishability of feature is adopted based on label set for calculating feature weighting,which will measure the importance degree of feature.Finally,all features are sorted by the value of feature weighting.Experiment was conducted on four multi-label datasets,and four evaluation criteria were used to mea-sure the effectiveness of our method.Experimental results show that the proposed algorithm is superior to several state-of-the-art multi-label feature selection algorithms.

Key words: Feature selection,Label weighting,Classification margin,Multi-label classification

[1] SCHAPIRE R,SINGER Y.BoosTexter:A boosting-based system for text categorization [J].Machine Learning,2000,39(2/3):135-168.
[2] ZHANG M,ZHOU Z.Multi label neural networks with applications to functional genomics and text categorization [J].IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1338-1351.
[3] BOUTELL M,LUO J,SHEN X,et al.Learning multi-labelscene classification [J].Pattern Recognition,2004,37(9):1757-1771.
[4] ZHENG X Y,ZHANG H X.Multiple Label Approach Based on Local Correlation of Neighbors[J].Computer Science,2014,41(2):123-126.(in Chinese) 郑希源,张化祥.基于局部近邻相关性的多标记算法[J].计算机科学,2014,41(2):123-126.
[5] 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.(in Chinese) 何志芬,杨明,刘会东.多标记分类和标记相关性的联合学习[J].软件学报,2014,25(9):1967-1981.
[6] HOTELLING H.Relations between two sets of variates [J].Biometrika,1936,28(3/4):321-377.
[7] ZHANG Y,ZHOU Z.Multi-Label dimensionality reduction via dependence maximization [J].Transactions on Knowledge Discovery from Data,2010,4(3):21-41.
[8] YU K,YU S,TRESP V.Multi-label informed latent semanticindexing [C]∥Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,NY:ACM,2005:258-265.
[9] LIU J H,LIN M L,WANG C X,et al.Multi-label Feature Selection Algorithm Based on Local Subspace[J].Pattern Recognition and Artificial Intelligence,2016,29(3):240-251.(in Chinese) 刘景华,林梦雷,王晨曦,等.基于局部子空间的多标记特征选择算法[J].模式识别与人工智能,2016,29(3):240-251.
[10] LIN Y,HU Q,LIU J,et al.Multi-label feature selection based on max-dependency and min-redundancy [J].Neurocomputing,2015,168(c):92-103.
[11] LIN Y,HU Q,LIU J.et al.Multi-Label Feature Selection Based on Neighborhood Mutual Information [J].Applied Soft Computing,2016,38(c):244-256.
[12] WANG C X,LIN M L,LIU J H,et al.Multi-label feature selection via fusing feature ranking[J].Computer Engineering and Applications,2016,52(17):93-100.(in Chinese) 王晨曦,林梦雷,刘景华,等.融合特征排序的多标记特征选择算法[J].计算机工程与应用,2016,52(17):93-100.
[13] ZHANG L,HU Q,DUAN J,et al.Multi-label Feature Selection with Fuzzy Rough Sets [M]∥Rough Sets and Knowledge Technology.Springer International Publishing,2014:121-128.
[14] DUAN J,HU Q H,ZHANG L J,et al.Feature Selection for Multi-Label Classification Based on Neighborhood Rough Set[J].Journal of Computer Research and Development,2015,52(1):56-65.(in Chinese) 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[J].计算机研究与发展,2015,52(1):56-65.
[15] SPOLAOR N,CHERMAN E,MONARD M.Using ReliefF for multi-label feature selection[C]∥Conferencia Latinoamericana de Informática.2011:960-975.
[16] SPOLAOR N,CHERMAN E,MONARD M,et al.A comparison of multi-label feature selection methods using the problem transformation approach[J].Electronic Notes in Theoretical Computer Science,2013,292:135-151.
[17] SPOLAOR N,CHERMAN E,MONARD M,et al.ReliefF for multi-label feature selection[C]∥2013 Brazilian Conference on Intelligent Systems (BRACIS).IEEE,2013:6-11.
[18] REYES O,MORELL C,VENTURA S.Scalable extensions ofthe ReliefF algorithm for weighting and selecting features on the multi-label learning context [J].Neurocomputing,2015,161:168-182.
[19] LI J H,FU J F,JIANG W J,et al.Feature Selection Method Based on MRMR for Text Classification[J].Computer Science,2016,43(10):225-228.(in Chinese) 李军怀,付静飞,蒋文杰,等.基于MRMR的文本分类特征选择方法[J].计算机科学,2016,43(10):225-228.
[20] SUN Y.Iterative RELIEF for feature weighting:algorithms,theories,and applications [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1035-1051.
[21] GILAD-BACHRACH R,NAVOT A,TISHBY N.Margin based feature selection-theory and algorithms [C]∥Proceedings of the Twenty-first International Conference on Machine Learning.ACM,2004:43.
[22] TSOUMAKAS G,VLAHAVAS I.Random k-label sets:An ensemble method for multi-label classification [C]∥ European Conference on Machine Learning.2007:406-417.
[23] ZHANG M,PEA J,ROBLES V.Feature selection for multi-label naive Bayes classification [J].Information Sciences,2009,179(19):3218-3229.
[24] ZHANG M,ZHOU Z.ML-KNN:A lazy learning approach to multi-label learning [J].Pattern Recognition,2007,40(7):2038-2048
[25] FRIEDMAN M.A comparison of alternative tests of significance for the problem of m rankings [J].The Annals of Mathematical Statistics,1940,11(1):86-92.
[26] DUNN O.Multiple comparisons among means [J].Journal of the American Statistical Association,1961,56(293):52-64.

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