example applications of rough sets theory a survey n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Example Applications of Rough Sets Theory – A Survey PowerPoint Presentation
Download Presentation
Example Applications of Rough Sets Theory – A Survey

Loading in 2 Seconds...

play fullscreen
1 / 36

Example Applications of Rough Sets Theory – A Survey - PowerPoint PPT Presentation


  • 163 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Example Applications of Rough Sets Theory – A Survey' - timothy-hayden


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
Introduction
  • Research on the application of Rough Sets Theory
  • Discovering possible areas of application
  • Further understanding of Rough Sets Theory usage
references
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
fault diagnosis using rough sets theory
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
fault diagnosis using rough sets theory1
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
fault diagnosis using rough sets theory2
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
fault diagnosis using rough sets theory6
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
software safety analysis using rough sets
Software Safety Analysis using Rough Sets
  • Investigates the safety aspects of computer software in safety-critical applications
  • Assessment of software safety using qualitative evaluations
software safety analysis using rough sets1
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
software safety analysis using rough sets4
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
software safety analysis using rough sets5
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%
economic and financial prediction using rough sets model
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
economic and financial prediction using rough sets model1
Economic and Financial Prediction using Rough Sets Model
  • Business failure prediction
    • ETEVA
  • Database Marketing
  • Financial Investment
    • TSE
using rough set theory to predict german word stress
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
using rough set theory to predict german word stress1
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
using rough set theory to predict german word stress2
Using Rough Set Theory to Predict German Word Stress
  • Corpus
    • Monomorphemic words
    • At least 2 non-schwa syllables
    • Nouns
    • 242 words
using rough set theory to predict german word stress3
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
using rough set theory to predict german word stress4
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
unsupervised rough set classification using gas
Unsupervised Rough Set Classification using GAs
  • Rough Set classification using Genetic Algorithms
  • Highway classification based on predominant usage
unsupervised rough set classification using gas1
Unsupervised Rough Set Classification using GAs
  • Applications of GAs
    • Job shop scheduling
    • Training neural nets
    • Image feature extraction
    • Image feature identification
unsupervised rough set classification using gas6
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
unsupervised rough set classification using gas7
Unsupervised Rough Set Classification using GAs
  • Total error of rough set classification is the weighted sum of these errors
unsupervised rough set classification using gas8
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
unsupervised rough set classification using gas9
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
conclusion
Conclusion
  • Triggering a better understanding of Rough Sets Theory
  • Opening eyes to different fields of application