南京大学学报(自然科学版) ›› 2016, Vol. 52 ›› Issue (2): 270.
梁新彦1,2,钱宇华1,2*,郭 倩2,成红红1,2
Liang Xinyan1,2,Qian Yuhua1,2*,Guo Qian2,Cheng Honghong1,2
摘要: 多标记学习研究的是一个对象同时具有多个标记的一类复杂问题.文本标注、视频内容标注、图像识别和蛋白质功能的发现等都属于这类任务.与单标记学习问题一样,多标记学习也遭遇到了数据维数大的挑战.针对多标记数据,目前已经设计出一些约简算法,但与单标记约简算法相比,方法数量有限且局限性大.随着大数据时代的到来,收集大量样本越来越容易,但标注收集到的全部样本不切实际.这给想要通过利用粗糙集模型来解决多标记学习问题的研究人员带来了三个挑战:数据维数更高、现有粗糙集的局限性和部分标记决策表的出现.为了解决这三个挑战,提出了面向多标记学习的局部粗糙集模型,并获得了一些有意思的性质.最后,通过利用局部粗糙集模型,设计了一个多标记的启发式约简算法,并在三个公开的多标记数据集上验证了算法的有效性.
[1] Tsoumakas G,Katakis I.Multilable classification:An overview.International Journal of Data Warehousing and Mining,2007,3(3):1-13. [2] Schapire R E,Singer Y.Boostexter:A boostingbased system for text categorization.Machine Learning,2000,39(2):135-168. [3] Boutell M R,Luo J,Shen X,et al.Learning multilabel scene classification.Pattern Recognition,2004,37(9):1757-1771. [4] Bao B K,Ni B,Mu Y,et al.Efficient regionaware large graph construction towards scalable multilabel propagation.Pattern Recognition,2011,44(3):598-606. [5] Read J,Pfahringer B,Holmes G,et al.Classifier chains for multilabel classification.Machine learning,2011,85(3):333-359. [6] Tsoumakas G,Katakis I,Vlahavas I.Random klabelsets for multilabel classification.IEEE Transaction On Knowledge and Data Engineering,2011,23(7):1079-1089. [7] Read J.A pruned problem transformation method for multilabel classification.In:Proceedings of 2008 New Zealand Computer Science Research Student Conference.Berlin:Springer,2008,143-150. [8] Zhang M,Zhou Z.MLKNN:A lazy learning approach to multilabel learning.Patter Recognition,2007,40(7):2038-2048. [9] Clare A,King R D.Knowledge discovery in multilabel phenotype data.Principles of data mining and knowledge discovery.Springer Berlin Heidelberg,2001,42-53. [10] Freund Y,Schapire R.A decisiontheoretic generalization of online learning and an application to boosting.Journal of computer and system sciences,1997,55(1):119-139. [11] De Comité F,Gilleron R,Tommasi M.Learning multilabel alternating decision trees from texts and data.Machine Learning and Data Mining in Pattern Recognition.Berlin:Springer,2003,35-49. [12] Zhang M,Zhou Z.Multilabel neural networks with applications to functional genomics and text categorization.IEEE Transactions on Knowledge and Data Engineering.2006,18(10):1338-1351. [13] Pawlak Z.Rough sets.International Journal of Computer and Information Science,1982,11:341-356. [14] Qian Y,Liang J,Pedrycz W,et al.Positive approximation:An accelerator for attribute reduction in rough set theory.Artificial Intelligence,2010,174(9):597-618. [15] Qian Y,Liang J,Pedrycz W,et al.An efficient accelerator for attribute reduction from incomplete data in rough set framework.Patter Recognition,2011,44(8):1658-1670. [16] Lin T,Huang K,Liu Q,et al.Rough sets,neighborhood systems and approximation.In:Proceeding of the Fourth International Symposium on Methodologies of Intelligent System.New York:NorthHolland,1990,130-141. [17] Hu Q,Yu D,Liu J,et al.Neighborhood rough set based heterogeneous feature subset selection.Information Science,2008,178(18):3577-3594. [18] Ziarko W.Variable precision rough set model.Journal of Computer and System Sciences,1993,46(1):39-59. [19] Zhang Y,Zhou Z.Multilabel dimensionality reduction via dependence maximization.ACM Transactions on Knowledge Discovery from Data (TKDD),2010,4(3):1-21. [20] 葛 雷,李国正,尤鸣宇.多标记学习的嵌入式特征选择.南京大学学报(自然科学),2009,45(5):671-676.(Ge L,Li G Z,You M Y.Embedded feature selection for multilabel learning.Journal of Nanjing University(Natural Sciences),2009,45(5):671-676.) [21] 张振海,李士宁,李志刚等.一种基于信息熵的多标签特征选择算法.计算机研究与发展,2013,50(6):1177-1184.(Zhang Z H,Li S N,Li Z G,et al.Multilabel feature selection algorithm based on information entropy.Journal of Computer Research and Development,2013,50(6):1177-1184.) [22] 张文修,徐宗本,梁 怡等.包含度理论.模糊系统与数学,1996,10(4):1-9.(Zhang W X,Xu Z B,Liang Y,et al.Inclusion degree theory.Fuzzy Systems and Mathematics,1996,10(4):1-9.) |
No related articles found! |
|