全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

适于渐变概念漂移数据的自适应分类算法

, PP. 623-633

Keywords: 渐变概念漂移,自适应分类,支持向量机

Full-Text   Cite this paper   Add to My Lib

Abstract:

数据的概念漂移特性是广泛存在的。针对渐变概念漂移的分类问题,提出一种自适应近邻投影均值差支持向量机算法。该算法基于结构风险最小化模型,以再生核Hilbert空间中近邻投影均值差为相邻分类器间差异的度量,在全局优化中融入数据自身的分布特征,提高算法的适应性。在模拟数据和真实数据集上的实验结果表明该算法是有效的。

References

[1]  Baena-García M,Campo-vila J D,Fidalgo R,et al. Early Drift Detection Method // Proc of the 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams. Berlin,Germany,2006: 77-86
[2]  Ko A H R,Sabourin R. From Dynamic Classifier Selection to Dynamic Ensemble Selection. Pattern Recognition,2008,41(5): 1718-1731
[3]  Tsymbal A,Pechenizkiy M,Cunningham P,et al. Dynamic Integration of Classifiers for Handling Concept Drift. Information Fusion,2008,9(1): 56-68
[4]  Wu Dengyuan,Wang Kai,He Tao,et al. A Dynamic Weighted Ensemble to Cope with Concept Drifting Classification // Proc of the 9th International Conference for Young Computer Scientists. Zhangjiajie,China,2008: 1854-1859
[5]  Kolter J Z,Maloof M A. Using Additive Expert Ensembles to Cope with Concept Drift // Proc of the 22nd International Conference on Machine Leaning. Bonn,Germany,2005: 449-456
[6]  Street W N,Kim Y S. A Streaming Ensemble Algorithm SEA for Large-Scale Classification // Proc of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco,USA,2001: 377-382
[7]  Sun Yue,Mao Guojun,Liu Xu,et al. Mining Concept Drifts from Data Streams Based on Multi-Classifiers. Acta Automatica Sinica,2008,34(1): 93-97 (in Chinese)(孙 岳,毛国君,刘 旭 ,等.基于多分类器的数据流中的概念漂移挖掘.自动化学报,2008,34(1): 93-97)
[8]  Masud M M,Gao J,Khan L,et al. A Multi-Partition Multi-Chunk Ensemble Technique to Classify Concept-Drifting Data Streams // Proc of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Bangkok,Thailand,2009: 363-375
[9]  Lazarescu M M,Venkatesh S,Bui H H. Using Multiple Windows to Track Concept Drift.Intelligent Data Analysis,2004,8(1): 29-59
[10]  Grinblat G L,Uzal L C,Ceccatto H A,et al. Solving Nonstationary Classification Problems with Coupled Support Vector Machines. IEEE Trans on Neural Networks,2011,22(1): 37-51
[11]  Shai B D,Blitzer J,Crammer K,et al. Analysis of Representations for Domain Adaptation // Schlkopf B,Platt J,Hoffman T,eds. Advances in Neural Information Processing System. Cambridge,USA: MIT Press,2007: 137-144
[12]  Sriperumbudur B K,Gretton A,Fukumizu K,et al. Hilbert Space Embeddings and Metrics on Probability Measures. Journal of Machine Learning Research,2010,11(4): 1517-1561
[13]  Gretton A,Fukumizu K,Harchaoui Z,et al. A Fast Consistent Kernel Two-Sample Test.[EB/OL] [2012-5-1].http://www.is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/NIPS2009-Gretton_
[14]  pdf
[15]  Quanz B,Huan J. Large Margin Transductive Transfer Learning // Proc of the 18th ACM Conference on Information and Knowledge Management. New York,USA,2009: 1327-1336
[16]  Bruzzone L,Marconcini M. Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy. IEEE Trans on Pattern Analysis and Machine Intelligence,2010,32(5): 770-787
[17]  Grinblat G L,Granitto P M,Ceccatto H A. Time-Adaptive Support Vector Machines. IberoAmerican Joumal of Artificial Intelligence,2008,12(40): 39-50
[18]  Belkin M,Niyogi P,Sindhwani V,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples.Journal of Machine Learning Research,2006,7(11): 2399-2434
[19]  Wang Xiaoming,Wang Shitong. Ensemble Classifier Based on Minimum Class Variance SVM and Null Space Classifier. Pattern Recognition and Artificial Intelligence,2010,23(4): 441-449 (in Chinese) (王晓明,王士同.最小类方差支持向量机与零空间分类器的集成.模式识别与人工智能,2010,23(4): 441-449)
[20]  Tao Jianwen,Wang Shitong. Kernel Support Vector Machine for Domain Adaptation. Acta Automatica Sinica,2012,38(5): 797-881 (in Chinese)(陶剑文,王士同.领域适应核支持向量机.自动化学报,2012,38(5):797-881)
[21]  Chang C C,Lin C J. LIBSVM: A Library for Support Vector Machines. [EB/OL] [2010-10-26]. http://www.csie.ntu.edu.tw/~cjlin/ papers/libsvm.pdf
[22]  Harries M.Splice-2 Comparative Evaluation: Electricity Pricing. Technical Report,NSW-CSE-TR-9905. Sydney,Australia: University of South Wales,1999附录 式(5)的详细推导过程.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133