example applications of rough sets theory – a survey
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Example Applications of Rough Sets Theory – A Survey
Christopher ChretienLaurentian UniversitySudbury, OntarioCanadaOctober 2002
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Introduction
Research on the application of Rough Sets Theory
Discovering possible areas of applicationFurther understanding of Rough Sets
Theory usage
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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
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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
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Fault Diagnosis using Rough Sets Theory
4 states are studied among the signal characteristicsNormal state Intake valve clearance is too small Intake valve clearance is too largeExhaust valve clearance is too large
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Fault Diagnosis using Rough Sets Theory
3 sampling points selected to collect vibration signals1st cylinder head2nd cylinder headcentre of the piston stroke on the surface of
the cylinder block
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Fault Diagnosis using Rough Sets Theory
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Fault Diagnosis using Rough Sets Theory
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Fault Diagnosis using Rough Sets Theory
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Fault Diagnosis using Rough Sets Theory
6 attributesFrequency domain attributes: IF, CGTime domain attributes: IT, σ, Dx, α4
18 attributes for decision table1 decision attribute with 4 possible
values based on states
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Software Safety Analysis using Rough Sets
Investigates the safety aspects of computer software in safety-critical applications
Assessment of software safety using qualitative evaluations
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Software Safety Analysis using Rough Sets
Use of checklists to collect data on software quality
Waterfall modelProject PlanningSpecification of requirementsDesign Implementation and integrationVerification and validationOperation and maintenance
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Software Safety Analysis using Rough Sets
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Software Safety Analysis using Rough Sets
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Software Safety Analysis using Rough Sets
8 student teams developing safety-related softwareDevice control over the internetElevator controllerAir traffic control systemSystem satellite control system
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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%
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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
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Economic and Financial Prediction using Rough Sets Model
Business failure predictionETEVA
Database MarketingFinancial Investment
TSE
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Economic and Financial Prediction using Rough Sets Model
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Economic and Financial Prediction using Rough Sets Model
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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
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Using Rough Set Theory to Predict German Word Stress
Variable Precision Rough Sets ModelAn 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
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Using Rough Set Theory to Predict German Word Stress
CorpusMonomorphemic wordsAt least 2 non-schwa syllablesNouns242 words
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Using Rough Set Theory to Predict German Word Stress
Attributes: Typ, Onset, Hoeche, Laenge, Spannung, Coda
36 attributes in totalAttributes aligned ‘from right to left’Decision attribute with possible values of
final, penult and antepenult
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Using Rough Set Theory to Predict German Word Stress
1st experimentStress assignment operates from right to left
2nd experimentEstimate predictive accuracy
3rd experimentRemove length information
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Unsupervised Rough Set Classification using GAs
Rough Set classification using Genetic Algorithms
Highway classification based on predominant usage
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Unsupervised Rough Set Classification using GAs
Applications of GAsJob shop schedulingTraining neural nets Image feature extraction Image feature identification
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Unsupervised Rough Set Classification using GAs
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Unsupervised Rough Set Classification using GAs
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Unsupervised Rough Set Classification using GAs
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Unsupervised Rough Set Classification using GAs
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Unsupervised Rough Set Classification using GAs
Rough Set classification scheme1. Both uh and uk are in the same lower
approximation A(Xi).
2. Object uh is in a lower approximation and uk is in the corresponding upper approximation UA(Xi)
3. Both uh and uk are in the same upper approximation
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Unsupervised Rough Set Classification using GAs
Total error of rough set classification is the weighted sum of these errors
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Unsupervised Rough Set Classification using GAs
Rough classification of highwaysPTC sitesRoads classified on the basis of trip
purposes and trip length characteristicsClasses: commuter, business, long distance
and recreational highwaysTraffic patterns: hourly, daily, monthly
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Unsupervised Rough Set Classification using GAs
Experiment264 monthly traffic patterns on Alberta
highways (1987-1991)Rough genome consisted of 264 genesClasses: commuter/business, long distance,
recreational
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Conclusion
Triggering a better understanding of Rough Sets Theory
Opening eyes to different fields of application