Multi-Task Linear Dependency Modeling for drug-related webpages classification

In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental results show that Multi-Task Linear Dependency Modeling outperforms existing decision level and feature level combination methods and achieves the best performance.

[1]  Weiming Hu,et al.  A Novel Framework for Web Pages Classification , 2013, ICMT 2013.

[2]  Sebastian Thrun,et al.  Discovering Structure in Multiple Learning Tasks: The TC Algorithm , 1996, ICML.

[3]  Ayhan Demiriz,et al.  Linear Programming Boosting via Column Generation , 2002, Machine Learning.

[4]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Madasu Hanmandlu,et al.  Score level fusion of hand based biometrics using t-norms , 2010, 2010 IEEE International Conference on Technologies for Homeland Security (HST).

[6]  Jian-Huang Lai,et al.  Linear Dependency Modeling for Classifier Fusion and Feature Combination , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Pong C. Yuen,et al.  Linear dependency modeling for feature fusion , 2011, 2011 International Conference on Computer Vision.

[9]  Weiming Hu,et al.  Quality-Based Learning for Web Data Classification , 2014, AAAI.

[10]  Weiming Hu,et al.  Metadata-Based Clustered Multi-task Learning for Thread Mining in Web Communities , 2016, MLDM.

[11]  Anil K. Jain,et al.  Likelihood Ratio-Based Biometric Score Fusion , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Li Zi-qing.  Standardization of Face Image Sample Quality , 2009 .

[13]  Natalia A. Schmid,et al.  Image quality assessment for iris biometric , 2006, SPIE Defense + Commercial Sensing.

[14]  Anil K. Jain,et al.  Decision-Level Fusion in Fingerprint Verification , 2001, Multiple Classifier Systems.

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

[16]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[17]  Francesco Dinuzzo,et al.  Kernel machines with two layers and multiple kernel learning , 2010, ArXiv.

[18]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Ruiguang Hu,et al.  Drug related webpages classification using images and text information based on multi-kernel learning , 2015, International Symposium on Multispectral Image Processing and Pattern Recognition.

[20]  Josef Kittler,et al.  A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Ernest Valveny,et al.  Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Charles A. Micchelli,et al.  Learning Multiple Tasks with Kernel Methods , 2005, J. Mach. Learn. Res..

[23]  Hal Daumé,et al.  Learning Task Grouping and Overlap in Multi-task Learning , 2012, ICML.

[24]  Greg M. Allenby,et al.  A Hierarchical Bayes Model of Primary and Secondary Demand , 1998 .

[25]  Horst Bischof,et al.  Online multi-class LPBoost , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.