Multi-Agent Based Multi-Knowledge Acquisition Method for Rough Set

1. Multi-Agent Based Multi-KnowledgeAcquisition Method for Rough Set Yang Liu (PhD student) Blekinge Institute of Technology, Sweden Xi’an Jiaotong University, P. R. China PhD student

2. Agenda • Background • Motivation • Method & Results • Future work & Conclusion • Reference • Q & A Blekinge Institute of Technology

3. Background • Data mining & knowledge acquisition [4] • Large-scale problem • Decentralized data source • Requirement of compact knowledge • Rough set based method [1,2] • Reduction • Rule induction • Multi-agent technology [6] • Flexible architecture • Autonomy, activity, reactivity, mobility and sociality Blekinge Institute of Technology

4. Motivation • Multi-knowledge acquisition method • Single reduction algorithm • Polynomial, knowledge is monotonous • All reduction algorithm • NP-Hard, knowledge is complete • Multiple reduction algorithm [3] • Polynomial, knowledge is sub-optimal • Multi-agent based data mining schema [5] • Decentralized data source • Divide and conquer method • Distributed data mining implementation Blekinge Institute of Technology

5. Method & results • Multi-reduction algorithm • Information system partition • Multi-agent based knowledge acquisition architecture • MAMKA system • Visualization • Experimental environment Blekinge Institute of Technology

6. Multi-reduction algorithm • Ideas • If only one reduct is selected to induce rules, some useful information hidden in other reducts may lose unavoidable • Multiple candidate reduction construction • Start with |C| candidates Add candidate partial-reducts Form reducts using greedy strategy Blekinge Institute of Technology

7. Partition of information system • Criteria • Independent, minimum losing of information • Three methods • Natural partition • Assume that the information system is partitioned independently • Reducts based partition • Information system is partitioned by multiple reducts • Attribute based partition • Information system is partitioned by a set of attributes Blekinge Institute of Technology

8. Multi-agent based knowledge acquisition architecture • MAS=<SP,AGlocal, AGglobal>. • Local agents • Extract multiple reducts • Form interactive rules • Global agents • Evaluates interactive knowledge in global datasets • Classification module • Classification • Matching score: complete & partial matching score Blekinge Institute of Technology

9. Results • MAMKA system • Visualization • Experimental environment Blekinge Institute of Technology

10. MAMKA System • Architecture • Agents are implemented by Java Agent Development Framework • Front-end: Tomcat web server + JSP + Servlet • Aims to support standards interface • WEB2.0, Web Service and J2ME • Standardized visualization • HTML & XML results • Integrated environment • Experiments • Decision-making environment Blekinge Institute of Technology

11. Overview of MAMKA Blekinge Institute of Technology

12. Decision table visualization • <Filename>.data & <Filename>.names are converted to html or xml representation for visualization Figure: names file’s html format Figure : data file’s html format Blekinge Institute of Technology

13. Rough set manipulation support • Multi-reduction algorithm Figure: data file (UCI format) Figure: multi-reduction code Figure: attribute description (dictionary KDD format) Blekinge Institute of Technology

14. Generated decision rules Figure: generated rules from PIMA dataset Blekinge Institute of Technology

15. Feature selection Figure: ranks of attributes from PIMA dataset Blekinge Institute of Technology

16. Integrated experimental environment (1) • Classification results visualization • Correct rate • Error rate • Refuse rate • Distribution of results Blekinge Institute of Technology

17. Integrated experimental environment (2) Figure: ten-fold cross validation results on PIMA dataset Blekinge Institute of Technology

18. Future work • Meta-mining architecture for diabetic health-care system • Agent based miner • Project plan • Continued… Blekinge Institute of Technology

19. Milestones and Deliverables – ROughSEt based Meta-mining system • 3Q 2008 • A study report on some typical diabetic care scenarios based on national-wide hospital. • A study report on measurements used to test the diabetes by diabetic experts. • 4Q 2005 • A rough set model for describing the complex relationships between diabetic symptom and patient’s behavior. • A prototype demonstrating the technologies for generating knowledge of diabetic diagnosis. • 1Q & 2Q 2009 • A study report on distributed data processing in E-Health information system. • A prototype demonstrating the technologies for health records in decentralized and heterogeneous database. • A prototype demonstrating the technologies for optimizing medical service. • 3Q & 4Q 2009 • A study report on the assessment of applying these technologies to the typical medical-care scenarios. • A prototype demonstrating the technologies for personalized self-care medical service. • A study report on the assessment of applying these technologies to the real-world medical-care scenarios. • This project will deliver 3+ papers for international journals or conferences and 1+ patent disclosures.

20. Project Plan - ROSEM Tasks • Survey Report 1. Study typical diabetic care scenarios in national-wide hospital 2. Study vital measurements in testing diabetes • Survey Report 3. Collect simulation data from experts • Fuzzy-rough set based diabetic model 4. Design fuzzy-rough set based diabetic behavior model • Knowledge acquisition system prototype 5. Develop knowledge acquisition method 6. Study on standard health record • Web based medical expert system prototype 7. Develop medical knowledge acquisition platform • Paper for internal review and discussion 8. Finish a paper on medical knowledge acquisition system 9. Survey existing IT technologies in E-Health home care • Intelligent diabetic monitoring mechanism 10. Develop intelligent monitoring service for elderly diabetics self-management • Diabetic home care web platform prototype 11. Develop home-care integrated platform prototype • Assessment report 12. Perform new technologies assessment 13. Finish a paper on intelligent monitoring and communication method in diabetic home care • Intelligent monitoring paper 14. Patent Authoring

21. References • [1] Z. Pawlak, “Rough Sets,” International Journal of Computer and Information Sciences, 1982, pp. 341-356.  • [2] Z. Pawlak, Rough Sets. Theoretical Aspects of Reasoning About Data, Dordrecht, Boston, London: Kluwer Academic Publishers, 1991.  • [3] Q. Wu, Bell, and David, “Multi-knowledge extraction and application,” Lecture Notes in Artificial Intelligence, vol. 2639, 2003, pp. 274 – 278.  • [4] M. Klusch, S. Lodi, and G. Moro, “Issues of agent-based distributed data mining,” International Conference on Autonomous Agents, 2003, pp. 1034 – 1035.  • [5] Gorodetsky et al., “Multi-agent technology for distributed data mining and classification,” Proceedings IEEE/WIC International Conference on Intelligent Agent Technology (IAT 2003), Canada: 2003, pp. 438 – 41.  • [6] M. Wooldridge and N. Jennings, “Intelligent Agents: Theory and Practice,” Knowledge Engineering Review, 1995 [http://www.doc.mmu.ac.uk/STAFF/mike/ker95/ker95-html.html], vol. 10, 1995; http://www.doc.mmu.ac.uk/STAFF/mikew.html. Blekinge Institute of Technology

22. Q & A • This document is available at: • http://www.esnips.com/web/liuyang2006-Documentation • This document can also be found by • Email to: liuyang2006@gmail.com • Thank you! YANG LIU (刘洋), School of engineering Blekinge Institute of Technology Ronneby, 372 25, Sweden Yang.liu@bth.se Blekinge Institute of Technology