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Example Applications of Rough Sets Theory – A Survey

Example Applications of Rough Sets Theory – A Survey. Christopher Chretien Laurentian University Sudbury, Ontario Canada October 2002. Introduction. Research on the application of Rough Sets Theory Discovering possible areas of application Further understanding of Rough Sets Theory usage.

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Example Applications of Rough Sets Theory – A Survey

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  1. Example Applications of Rough Sets Theory – A Survey Christopher Chretien Laurentian University Sudbury, Ontario Canada October 2002

  2. Introduction • Research on the application of Rough Sets Theory • Discovering possible areas of application • Further understanding of Rough Sets Theory usage

  3. References • Lixiang Shen, Francis E. H. Tay, Liangsheng Qu and Yudi Shen (2000), Fault Diagnosis using Rough Sets Theory , Computers in Industry, vol. 43, Issue 1, 1 August 2000, pp.61-72., URL:www.geocities.com/roughset/Fault_diagnosis_using_rough_sets_theory.pdf • Israel E. Chen-Jimenez, Andrew Kornecki, Janusz Zalewski, Software Safety Analysis Using Rough Sets, URL:http://www-ece.engr.ucf.edu/~jza/classes/6885/rough.ps • Francis E. H. Tay and Lixiang Shen (2002), Economic and Financial Prediction using Rough Sets Model , European Journal of Operational Research 141, pp.643-661, URL:http://www.geocities.com/roughset/EJOR.pdf • Pawan Lingras (2001), Unsupervised Rough Set Classification Using GAs Journal of Intelligent Information Systems, 16, 215–228, found on: CiteSeer, URL:http://citeseer.nj.nec.com/cs • Rapp, S., Jessen, M. and Dogil, G. (1994). Using Rough Sets Theory to Predict German Word Stress. in: Nebel, B. and Dreschler-Fischer, L. (Eds.) KI-94: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence 861, Springer-Verlag, URL:www.ims.uni-stuttgart.de/~rapp/ki94full.ps

  4. Fault Diagnosis using Rough Sets Theory • Diagnosis of a valve fault for a multi-cylinder diesel engine • Rough Sets Theory is used to analyze the decision table composed of attributes extracted from the vibration signals

  5. Fault Diagnosis using Rough Sets Theory • 4 states are studied among the signal characteristics • Normal state • Intake valve clearance is too small • Intake valve clearance is too large • Exhaust valve clearance is too large

  6. Fault Diagnosis using Rough Sets Theory • 3 sampling points selected to collect vibration signals • 1st cylinder head • 2nd cylinder head • centre of the piston stroke on the surface of the cylinder block

  7. Fault Diagnosis using Rough Sets Theory

  8. Fault Diagnosis using Rough Sets Theory

  9. Fault Diagnosis using Rough Sets Theory

  10. Fault Diagnosis using Rough Sets Theory • 6 attributes • Frequency domain attributes: IF, CG • Time domain attributes: IT, σ, Dx, α4 • 18 attributes for decision table • 1 decision attribute with 4 possible values based on states

  11. Software Safety Analysis using Rough Sets • Investigates the safety aspects of computer software in safety-critical applications • Assessment of software safety using qualitative evaluations

  12. Software Safety Analysis using Rough Sets • Use of checklists to collect data on software quality • Waterfall model • Project Planning • Specification of requirements • Design • Implementation and integration • Verification and validation • Operation and maintenance

  13. Software Safety Analysis using Rough Sets

  14. Software Safety Analysis using Rough Sets

  15. Software Safety Analysis using Rough Sets • 8 student teams developing safety-related software • Device control over the internet • Elevator controller • Air traffic control system • System satellite control system

  16. Software Safety Analysis using Rough Sets • 150 questions about the first 5 phases of the waterfall model • Overall safety level for 6 of the 8 projects was around 60%

  17. Economic and Financial Prediction using Rough Sets Model • Applications of Rough Sets model in economic and financial prediction • Emphasis on main areas of business failure prediction, database marketing and financial investment

  18. Economic and Financial Prediction using Rough Sets Model • Business failure prediction • ETEVA • Database Marketing • Financial Investment • TSE

  19. Economic and Financial Prediction using Rough Sets Model

  20. Economic and Financial Prediction using Rough Sets Model

  21. Using Rough Set Theory to Predict German Word Stress • Prediction of German word stress by extracting symbolic rules from sample data • Symbolic rules are induced with a machine learning approach based on Rough Sets Theory

  22. Using Rough Set Theory to Predict German Word Stress • Variable Precision Rough Sets Model • An elementary class belongs to RβX iff a (100% - β) majority of it’s elements belongs to X • An elementary class does not belong to URβX iff a (100% - β) majority of its elements does not belong to X

  23. Using Rough Set Theory to Predict German Word Stress • Corpus • Monomorphemic words • At least 2 non-schwa syllables • Nouns • 242 words

  24. Using Rough Set Theory to Predict German Word Stress • Attributes: Typ, Onset, Hoeche, Laenge, Spannung, Coda • 36 attributes in total • Attributes aligned ‘from right to left’ • Decision attribute with possible values of final, penult and antepenult

  25. Using Rough Set Theory to Predict German Word Stress • 1st experiment • Stress assignment operates from right to left • 2nd experiment • Estimate predictive accuracy • 3rd experiment • Remove length information

  26. Unsupervised Rough Set Classification using GAs • Rough Set classification using Genetic Algorithms • Highway classification based on predominant usage

  27. Unsupervised Rough Set Classification using GAs • Applications of GAs • Job shop scheduling • Training neural nets • Image feature extraction • Image feature identification

  28. Unsupervised Rough Set Classification using GAs

  29. Unsupervised Rough Set Classification using GAs

  30. Unsupervised Rough Set Classification using GAs

  31. Unsupervised Rough Set Classification using GAs

  32. Unsupervised Rough Set Classification using GAs • Rough Set classification scheme • Both uh and uk are in the same lower approximation A(Xi). • Object uh is in a lower approximation and uk is in the corresponding upper approximation UA(Xi) • Both uh and uk are in the same upper approximation

  33. Unsupervised Rough Set Classification using GAs • Total error of rough set classification is the weighted sum of these errors

  34. Unsupervised Rough Set Classification using GAs • Rough classification of highways • PTC sites • Roads classified on the basis of trip purposes and trip length characteristics • Classes: commuter, business, long distance and recreational highways • Traffic patterns: hourly, daily, monthly

  35. Unsupervised Rough Set Classification using GAs • Experiment • 264 monthly traffic patterns on Alberta highways (1987-1991) • Rough genome consisted of 264 genes • Classes: commuter/business, long distance, recreational

  36. Conclusion • Triggering a better understanding of Rough Sets Theory • Opening eyes to different fields of application

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