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TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents

TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents. Haimonti Dutta 1 , Xianshu Zhu 2 , Tushar Muhale 2 , Hillol Kargupta 2 , Kirk Borne 3 , Codrina Lauth 4 , Florian Holz 5 , and Gerherd Heyer 5. 1 Columbia University

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TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents

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  1. TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents Haimonti Dutta1, Xianshu Zhu2, Tushar Muhale2, Hillol Kargupta2, Kirk Borne3, Codrina Lauth4, Florian Holz5, and Gerherd Heyer5 1Columbia University 2University of Maryland, Baltimore County 3George Mason University 4Fraunhofer Institute for Intelligent Analysis and Information Systems 5University of Leipzig

  2. Outline • Introduction and Motivation • Related Work • TagLearner • Distributed Classifier-learning Algorithm • Experiments • Conclusion and Future Work

  3. Introduction • Large Online Document Repositories: • Online Newspapers, Digital Libraries, etc. • Growing in size • Text categorization on the repositories: • No automated text classification mechanism • Performed by authorities, such as librarians Impractical

  4. Introduction (cont.) • Collaborative tagging • Del.icio.us, Flickr, Google image labeler • Recruit web users to add tags to a resource • Help to utilize power of people’s knowledge • Pros and cons • Improve web search result, help on classification • Not support by most online text repositories • Lack of control • Absence of standard keywords • Errors in tagging due to spelling errors • Harder to manage due to increased content diversity

  5. Motivation • Provide automated classification service • Utilize collaborative effort of users • Collaborative tagging in Peer-to-Peer network • Without repositories’ support P2P Classifier learning system

  6. Related Work • Collaborative tagging: • Recommendation System (Tso-Sutter et al.) • Web search (Yahia et al.) • Classification accuracy (Brooks et al.) • Distributed Linear Programming: • Distributed Simplex Algorithm (Dutta et al.)

  7. TagLearner: A P2P Classifier Learning System

  8. TagLearner: A P2P Classifier Learning System

  9. TagLearner: A P2P Classifier Learning System

  10. TagLearner: A P2P Classifier Learning System

  11. TagLearner • Register service by creating a tagging group • Maintain a tagging group for this service • Predefined Labels used for tagging • Features for classification • Group members • Learnt classifier model Service provider: provide P2P classifier learning service

  12. TagLearner • Interface: - Join or leave the tagging group - Tag the web documents • Distributed classifier learning algorithm Client side browser plugin

  13. Class 2 Class 1 Classifier Design by Linear Programming • Classification problem can be framed as a linear programming problem :feature vector of k-th instance W : weight vector We want to find a W such that: W can be found by minimizing the error

  14. Classifier Design by Linear Programming • Maximize: Subject to: where Use Simplex Method to solve it!

  15. + + = w 4 w 2 w 0 . 5 1 2 3 Distributed Linear Programming • Distributed data • Each user only has a collection of constraints • Objective function: • Constraints: Simplex Tableau

  16. Distributed Simplex Algorithm User A User B User C User D Each user has different constraints, but wants to solve the same objective function.

  17. Distributed Simplex Algorithm User A User B User C User D

  18. Distributed Simplex Algorithm User A User B User C User D 0.5/3=1/6 0.5/2=1/4 0.5/7=1/14 0.5/3=1/6 0.5/6.5=13/4

  19. Distributed Simplex Algorithm User A User B User C User D 0.5/3=1/6 0.5/2=1/4 0.5/7=1/14 0.5/3=1/6 0.5/6.5=13/4

  20. Experimental Results • Distributed Data Mining Toolkit (DDMT) • “NSF Research Awards Abstracts 1990-2003” data set from the UCI Machine Learning Repository • We only consider abstracts belonging to Earth and Mathematical sciences • Features used for classification do not rely on collaboratively generated annotations.

  21. Experiments (cont.) Figure 1. Communication cost versus the number of nodes in the network

  22. Experiments (cont.)

  23. Conclusion and Future Work • Conclusion: • P2P classifier learning system prototype • Scalable distributed classification algorithm based on linear programming • Future work: • extension of the classification algorithm for multi-class classification problems • Improve classification accuracy

  24. Thank you !Questions ?

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