南京大学学报(自然科学版) ›› 2022, Vol. 58 ›› Issue (1): 6070.doi: 10.13232/j.cnki.jnju.2022.01.007
卢舜1,2, 林耀进1,2(), 吴镒潾1,2, 包丰浩1,2, 王晨曦1,2
Shun Lu1,2, Yaojin Lin1,2(), Yilin Wu1,2, Fenghao Bao1,2, Chenxi Wang1,2
摘要:
多标记学习广泛应用于图像分类、疾病诊断等领域,然而特征的高维性给多标记分类算法带来时间负担、过拟合和性能低等问题.基于多粒度邻域一致性设计相应的多标记特征选择算法:首先利用标记空间和特征空间邻域一致性来粒化所有样本,并基于多粒度邻域一致性观点定义新的多标记邻域信息熵和多标记邻域互信息;其次,基于邻域互信息构建一个评价候选特征质量的目标函数用于评价每个特征的重要性;最后通过多个指标验证了所提算法的有效性.
中图分类号:
1 | Boutell M R,Luo J B,Shen X P,et al. Learning multi?label scene classification. Pattern Recognition,2004,37(9):1757-1771. |
2 | Zhang P,Liu G X,Gao W F. Distinguishing two types of labels for multi?label feature selection. Pattern Recognition,2019(95):72-82. |
3 | Wold H. Estimation of principal components and related models by iterative least squares∥Krishnajah P R. Multivariate analysis. New York:Academic Press,1966:391-420. |
4 | Hotelling H. Relations between two sets of variates∥Kotz S,Johnson N L. Breakthroughs in statistics. Springer Berlin Heidelberg,1992:162-190. |
5 | Fukunaga K. Introduction to statistical pattern recognition. The 2nd Edition. New York:Academic Press,1990,592. |
6 | Gharroudi O,Elghazel H,Aussem A. A comparison of multi?label feature selection methods using the random forest paradigm∥Canadian Conference on Artificial Intelligence. Springer Berlin Heidelberg,2014:95-106. |
7 | Gu Q Q,Li Z H,Han J W. Correlated multi?label feature selection∥Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York,NY,USA:ACM,2011:1087-1096. |
8 | Slavkov I,Karcheska J,Kocev D,et al. Relieff for hierarchical multi?label classification∥Proceedings of the 2nd International Workshop on New Frontiers in Mining Complex Patterns. Springer Berlin Heidelberg,2013:148-161. |
9 | Zhang L J,Hu Q H,Duan J,et al. Multi?label feature selection with fuzzy rough sets∥Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Springer Berlin Heidelberg,2014:121-128. |
10 | Ding C,Peng H C. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology,2005,3(2):185-205. |
11 | Lee J,Kim D W. Mutual information?based multi?label feature selection using interaction information. Expert Systems with Applications,2015,42(4):2013-2025. |
12 | Li Y W,Lin Y J,Liu J H,et al. Feature selection for multi?label learning based on kernelized fuzzy rough sets. Neurocomputing,2018(318):271-286. |
13 | Lin Y J,Hu Q H,Liu J H,et al. Multi?label feature selection based on neighborhood mutual information. Applied Soft Computing,2016(38):244-256. |
14 | Zhang M L,Pe?a J M,Robles V. Feature selection for multi?label naive bayes classification. Information Sciences,2009,179(19):3218-3229. |
15 | Zhang Y,Zhou Z H. Multilabel dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data,2010,4(3):1-21. |
16 | Lee J,Kim D W. Feature selection for multi?label classification using multivariate mutual information. Pattern Recognition Letters,2013,34(3):349-357. |
17 | Spola?r N,Cherman E A,Monard M C,et al. ReliefF for multi?label feature selection∥2013 Brazilian Conference on Intelligent Systems. Fortaleza,Brazil:IEEE,2013:6-11. |
18 | Friedman M. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics,1940,11(1):86-92. |
19 | Dunn O J. Multiple comparisons among means. Journal of the American statistical Association,1961,56(293):52-64. |
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