You are previewing International Journal of Rough Sets and Data Analysis (IJRSDA) Volume 1, Issue 1.
O'Reilly logo
International Journal of Rough Sets and Data Analysis (IJRSDA) Volume 1, Issue 1

Book Description

The International Journal of Rough Sets and Data Analysis (IJRSDA) is a multidisciplinary journal that publishes high-quality and significant research in all fields of rough sets, granular computing and data mining techniques. Rough set theory is a mathematical approach concerned with the analysis and modeling of classification and decision problems involving vague, imprecise, uncertain, or incomplete information. Rough sets have been proposed for a variety of applications, including artificial intelligence and cognitive sciences, especially machine learning, knowledge discovery, data mining, expert systems, approximate reasoning, and pattern recognition. The journal extends existing research findings (theoretical innovations and modeling applications) to provide the highest quality original concepts, hybrid applications, innovative methodologies, and development trends studies for all audiences. This journal publishes original articles, reviews, technical reports, patent alerts, and case studies on the latest innovative findings of new methodologies and techniques.

This issue contains the following articles:

  • A Study on Bayesian Decision Theoretic Rough Set
  • Attribute Reduction Using Bayesian Decision Theoretic Rough Set Models
  • Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data
  • A Particle Swarm Optimization Approach to Fuzzy Case-based Reasoning in the Framework of Collaborative Filtering
  • Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory
  • Comparing and Contrasting Rough Set with Logistic Regression for a Dataset
  • Incremental Learning Researches on Rough Set Theory: Status and Future

Table of Contents

  1. Cover
  2. Masthead
  3. Call For Articles
  4. Editorial Preface
    1. REFERENCES
  5. A Study on Bayesian Decision Theoretic Rough Set
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. A STUDY ON VARIOUS TYPES OF ROUGH SETS
    4. 3. A COMPARATIVE STUDY BETWEEN PAWLAKS ROUGH SET AND BAYESIAN DECISION THEORETIC ROUGH SET MODEL
    5. 4. CONCLUSION
    6. REFERENCE
  6. Attribute Reduction Using Bayesian Decision Theoretic Rough Set Models
    1. ABSTRACT
    2. 2. ATTRIBUTE REDUCTION BY BAYESIAN DECISION THEORETIC ROUGH SET MODEL
    3. 3. COMPARATIVE STUDY WITH SOME OTHER MODELS USING THE HIV/AIDS EXAMPLE
    4. 4. CONCLUSION
    5. REFERENCES
  7. Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. AN EXEMPLARY NIS AND ISSUES IN RNIA
    4. 3. RNIA SOFTWARE TOOL IN C AND PROLOG
    5. 4. GETRNIA: A WEB SOFTWARE TOOL IN PYTHON
    6. 5. A NEW ISSUE IN RNIA
    7. 6. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
  8. A Particle Swarm Optimization Approach to Fuzzy Case-based Reasoning in the Framework of Collaborative Filtering
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. PROPOSED FRAMEWORK
    5. 4. EXPERIMENTS AND RESULTS
    6. 5. CONCLUSION
    7. REFERENCES
    8. ENDNOTES
  9. Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory
    1. ABSTRACT
    2. INTRODUCTION
    3. RELATED WORKS
    4. METHODS AND MATERIALS
    5. RESULTS
    6. DISCUSSION
    7. CONCLUSION
    8. REFERENCES
  10. Comparing and Contrasting Rough Set with Logistic Regression for a Dataset
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. REVIEW OF LOGISTIC REGRESSION AND LOGIT MODEL
    5. 4. ILLUSTRATION OF LOGISTIC REGRESSION ANALYSIS USING MONK’S DATA SET
    6. 5. ROUGH SETS
    7. 6. COMPARISON OF ROUGH SET WITH LOGISTIC REGRESSION
    8. 7. CONCLUSION AND FUTURE WORK
    9. REFERENCES
  11. Incremental Learning Researches on Rough Set Theory:
    1. ABSTRACT
    2. INTRODUCTION
    3. THE BASIC CONCEPTS OF ROUGH SETS
    4. INCREMENTAL LEARNING STRATEGIES ON THE VARIATION OF OBJECTS
    5. INCREMENTAL LEARNING STRATEGIES WITH THE VARIATION OF ATTRIBUTES
    6. INCREMENTAL LEARNING STRATEGIES WITH THE VARIATION OF ATTRIBUTE VALUES
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
  12. Call For Articles