One-Class Transfer Learning with Uncertain Data

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.

[1]  Takafumi Kanamori,et al.  Statistical outlier detection using direct density ratio estimation , 2011, Knowledge and Information Systems.

[2]  Chengqi Zhang,et al.  Similarity-Based Approach for Positive and Unlabeled Learning , 2011, IJCAI.

[3]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[4]  Neil D. Lawrence,et al.  Learning to learn with the informative vector machine , 2004, ICML.

[5]  David Wai-Lok Cheung,et al.  Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index , 2010, IEEE Transactions on Knowledge and Data Engineering.

[6]  Hans-Peter Kriegel,et al.  Density-based clustering of uncertain data , 2005, KDD '05.

[7]  Sabine Van Huffel,et al.  Total least squares problem - computational aspects and analysis , 1991, Frontiers in applied mathematics.

[8]  Philip S. Yu,et al.  One-Class-Based Uncertain Data Stream Learning , 2011, SDM.

[9]  Philip S. Yu,et al.  Building text classifiers using positive and unlabeled examples , 2003, Third IEEE International Conference on Data Mining.

[10]  Jianqiang Yang,et al.  Exploiting Uncertain Data in Support Vector Classification , 2007, KES.

[11]  Cheng Cheng,et al.  Finding pre-images via evolution strategies , 2011, Appl. Soft Comput..

[12]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[15]  Trung Le,et al.  Multiple distribution data description learning method for novelty detection , 2011, The 2011 International Joint Conference on Neural Networks.

[16]  Jiawei Han,et al.  PEBL: Web page classification without negative examples , 2004, IEEE Transactions on Knowledge and Data Engineering.

[17]  Philip S. Yu,et al.  A Survey of Uncertain Data Algorithms and Applications , 2009, IEEE Transactions on Knowledge and Data Engineering.

[18]  Qiang Yang,et al.  Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) Adaptive Transfer Learning , 2022 .

[19]  Massimiliano Pontil,et al.  Regularized multi--task learning , 2004, KDD.

[20]  Xiaoli Li,et al.  Learning to Classify Texts Using Positive and Unlabeled Data , 2003, IJCAI.

[21]  W. M. Shaw.  On the Foundation of Evaluation. , 1986 .

[22]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[23]  Hans-Peter Kriegel,et al.  Hierarchical density-based clustering of uncertain data , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[24]  Marimuthu Palaniswami,et al.  Spatio-temporal modelling-based drift-aware wireless sensor networks , 2011, IET Wirel. Sens. Syst..

[25]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[26]  Philip S. Yu,et al.  Text classification without negative examples revisit , 2006, IEEE Transactions on Knowledge and Data Engineering.

[27]  Philip S. Yu,et al.  A Framework for Clustering Uncertain Data Streams , 2008, 2008 IEEE 24th International Conference on Data Engineering.