Abstract
One-class learning aims at constructing a distinctive classifier based on the labeled one class data. However, it is a challenge for the existing one-class learning methods to transfer knowledge from a source task to a target task for uncertain data. To address this challenge, this paper proposes a novel approach, called uncertain one-class transfer learning with SVM (UOCT-SVM), which first formulates the uncertain data and transfer learning into one-class SVM as an optimization problem and then proposes an iterative framework to build an accurate classifier for the target task. Our proposed method explicitly addresses the problem of one-class transfer learning with uncertain data. Extensive experiments has found our proposed method can mitigate the effect of uncertain data on the decision boundary and transfer knowledge to help build an accurate classifier for the target task, compared with state-of-the-art one-class learning methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C., Yu, P.S.: A framework for clustering uncertain data streams. In: ICDE, pp. 150–159 (2008)
Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. TKDE 21(5), 609–623 (2009)
Cao, B., Pan, J., Zhang, Y., Yeung, D.Y., Yang, Q.: Adaptive transfer learning. In: AAAI (2010)
Fung, G.P.C., Yu, J.X., Lu, H., Yu, P.S.: Text classification without negative examples revisit. TKDE 18(6), 6–20 (2006)
Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T.: Statistical outlier detection using direct density ratio estimation. KAIS 26(2), 309–336 (2011)
Huffel, S.V., Vandewalle, J.: The total least squares problem: Computational aspects and analysis. Frontiers in Applied Mathematics, vol. 9. SIAM Press, Philadelphia (1991)
Yang, J., Gunn, S.: Exploiting uncertain data in support vector classification. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007/ WIRN 2007, Part III. LNCS (LNAI), vol. 4694, pp. 148–155. Springer, Heidelberg (2007)
Kao, B., Lee, S.D., Lee, F.K.F., Cheung, D.W., Ho, W.: Clustering uncertain data using voronoi diagrams and r-tree index. TKDE 22(9), 1219–1233 (2010)
Kriegel, H.P., Pfeifle, M.: Hierarchical density based clustering of uncertain data. In: ICDE, pp. 689–692 (2005)
Lawrence, N.D., Platt, J.C.: Learning to learn with the informative vector machine. In: ICML (2004)
Li, J., Su, L., Cheng, C.: Finding pre-images via evolution strategies. Applied Soft Computing 11(6), 4183–4194 (2011)
Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: IJCAI, pp. 587–592 (2003)
Liu, B., Dai, Y., Li, X., Lee, W.S., Yu, P.S.: Building text classifiers using positive and unlabeled examples. In: ICDM, pp. 179–186 (2003)
Liu, B., Xiao, Y., Cao, L., Yu, P.S.: One-class-based uncertain data stream learning. In: SDM, pp. 992–1003 (2011)
Pan, S.J., Tsand, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE TNN 22(2), 199–210 (2011)
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: ICML (2007)
Schölkopf, B., Platt, J., Taylor, J.S., Smola, A.J., Williamson, R.: Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471 (2001)
Takruri, M., Rajasegarar, S., Challa, S., Leckie, C., Palaniswami, M.: Spatio-temporal modelling-based drift-aware wireless sensor networks. Wireless Sensor Systems 1(2), 110–122 (2011)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54(1), 45–66 (2004)
TEvgeniou, T., Pontil, M.: Regularized multi–task learning. In: KDD (2004)
Trung, L., Dat, T., Phuoc, N., Wanli, M., Sharma, D.: Multiple distribution data description learning method for novelty detection. In: IJCNN, pp. 2321–2326 (2011)
Vapnik, V.: Statistical learning theory. Springer, London (1998)
William, J., Shaw, M.: On the foundation of evaluation. American Society for Information Science 37(5), 346–348 (1986)
Xiao, Y., Liu, B., Yin, J., Cao, L., Zhang, C., Hao, Z.: Similarity-based approach for positive and unlabeled learning. In: IJCAI, pp. 1577–1582 (2011)
Yu, H., Han, J., Chang, K.C.C.: Pebl: Web page classification without negative examples. TKDE 16(1), 70–81 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, B., Yu, P.S., Xiao, Y., Hao, Z. (2013). One-Class Transfer Learning with Uncertain Data. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-37453-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37452-4
Online ISBN: 978-3-642-37453-1
eBook Packages: Computer ScienceComputer Science (R0)