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Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems

Book Description

The consideration of symbolic machine learning algorithms as an entire class will make it possible, in the future, to generate algorithms, with the aid of some parameters, depending on the initial users’ requirements and the quality of solving targeted problems in domain applications.Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems surveys, analyzes, and compares the most effective algorithms for mining all kinds of logical rules. Global academics and professionals in related fields have come together to create this unique knowledge-sharing resources which will serve as a forum for future collaborations.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. EDITORIAL ADVISORY BOARD
    2. LIST OF REVIEWERS
  5. Preface
    1. MOTIVATION OF COMMONSENSE REASONING PROCESS
    2. KNOWLEDGE IS A MEANS OF DATA ORGANIZATION AND MANAGEMENT
    3. A GUIDED TOUR THROUGH THE CHAPTERS
    4. THE CONTRIBUTION OF THIS EDITED BOOK
  6. Acknowledgment
  7. Section 1:
    1. Chapter 1: Integrated Model of Inductive-Deductive Inference Based on Finite Predicates and Implicative Regularities
      1. ABSTRACT
      2. INTRODUCTION
      3. MATRIX FORM OF FINITE PREDICATES
      4. DATA AND KNOWLEDGE REPRESENTATION
      5. KNOWLEDGE DISCOVERY BY INDUCTIVE INFERENCE
      6. KNOWLEDGE BASE AND ITS SIMPLIFICATION
      7. RESOLUTION RULES
      8. DEDUCTIVE INFERENCE IN PATTERN RECOGNITION
      9. CONCLUSION
    2. Chapter 2: Solving Large Systems of Boolean Equations
      1. ABSTRACT
      2. INTRODUCTION
      3. SEARCH TREE MINIMIZATION
      4. REDUCTION RULES APPLIED TO A SYSTEM OF BOOLEAN EQUATIONS
      5. SYSTEMS OF LINEAR LOGICAL EQUATIONS: FINDING SHORTEST SOLUTIONS
      6. CONCLUSION
    3. Chapter 3: Constructing Galois Lattices as a Commonsense Reasoning Process
      1. ABSTRACT
      2. THE RULES OF THE FIRST TYPE IN THE FORM OF “IF–THEN” ASSERTIONS
      3. THE RULES OF THE FIRST TYPE AS A LANGUAGE FOR KNOWLEDGE
      4. THE RULES OF THE SECOND TYPE OR COMMONSENSE REASONING RULES
      5. CORRESPONDENCE OF GALOIS FOR GOOD CLASSIFICATION TEST DEFINITION
      6. GENERATING GALOIS LATTICES
      7. INDUCTIVE RULES FOR CONSTRUCTING GOOD CLASSIFICATION TESTS
      8. REDUCING THE RULES OF INDUCTIVE TRANSITIONS TO THE DEDUCTIVE AND INDUCTIVE COMMONSENSE REASONING RULES OF THE SECOND TYPE
      9. THE DECOMPOSITION OF GOOD TEST INFERRING INTO SUBTASKS
      10. SPECIAL OPERATIONS FOR FORMING SUBTASKS
      11. CONCLUSION
      12. APPENDIX
    4. Chapter 4: An Analytical Survey of Current Approaches to Mining Logical Rules from Data
      1. ABSTRACT
      2. INTRODUCTION
      3. NOTATIONS AND BASIC CONCEPTS
      4. DIAGNOSTIC TEST APPROACH TO MACHINE LEARNING PROBLEMS
      5. DIAGNOSTIC TEST APPROACH AND INFERRING FUNCTIONAL DEPENDENCIES
      6. CLASSIFICATION OF ITEMSET MINING ALGORITHMS
      7. MULTIFUNCTIONAL APPROACH TO FREQUENT ITEMSET GENERATION
      8. APPROACHES TO ADAPTIVE FREQUENT ITEMSET MINING
      9. CONCLUSION
    5. Chapter 5: Logical Inference and Defeasible Reasoning in N-tuple Algebra
      1. ABSTRACT
      2. INTRODUCTION
      3. BASICS OF N-TUPLE ALGEBRA
      4. LOGICAL INFERENCE IN NTA
      5. DEFEASIBLE REASONING IN NTA
      6. CONCLUSION
      7. APPENDIX: SOME NTA THEOREMS WITH PROOFS
  8. Section 2:
    1. Chapter 6: Measuring Human Intelligence by Applying Soft Computing Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY LOGIC BASED SYSTEMS
      4. EVOLUTIONARY COMPUTING
      5. HYBRIDIZATION OF FUZZY AND GENETIC APPROACH
      6. APPROACHES AND ESTABLISHED MODELS
      7. RELATED WORK IN THE AREA OF GENETIC FUZZY SYSTEMS (GFS)
      8. SAMPLE CASE: PROPOSED MODEL USING GFS FOR ANALYZING HUMAN INTELLIGENCE
      9. OUTCOME AND RESULTS
      10. CONCLUSION AND FUTURE WORK
    2. Chapter 7: Intelligent Data Processing Based on Multi-Dimensional Numbered Memory Structures
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. MEMORY MANAGEMENT AND ACCESS METHODS
      4. 5. PROGRAM REALIZATION
      5. 6. CONCLUSION
      6. 7. FUTURE RESEARCH DIRECTIONS
    3. Chapter 8: Bimodal Cross-Validation Approach for Recommender Systems Diagnostics
      1. ABSTRACT
      2. INTRODUCTION
      3. RECOMMENDER ALGORITHMS
      4. SIMILARITY MEASURES
      5. QUALITY RECOMMENDATIONS EVALUATION
      6. EXPERIMENT RESULTS
      7. EXAMPLE OF REAL RECOMMENDER APPLICATIONS
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
  9. Section 3:
    1. Chapter 9: Application of Machine Training Methods to Design of New Inorganic Compounds
      1. ABSTRACT
      2. INTRODUCTION
      3. STATEMENT OF THE PROBLEM OF DESIGNING NEW INORGANIC COMPOUNDS
      4. METHODS AND TOOLS
      5. APPLICATION OF PATTERN RECOGNITION METHODS TO INORGANIC CHEMISTRY AND MATERIALS SCIENCE
      6. RESULTS OF THE PREDICTION OF NEW INORGANIC COMPOUNDS
      7. CONCLUSION
    2. Chapter 10: Machine Learning in Studying the Organism’s Functional State of Clinically Healthy Individuals Depending on Their Immune Reactivity
      1. ABSTRACT
      2. INTRODUCTION
      3. CONSTRUCTING INTERVAL STRUCTURES
      4. INFERRING LOGICAL RULES AFTER INTERVAL ANALYSIS OF A DATA SET
      5. CONCLUSION
    3. Chapter 11: Business Intelligence in Corporate Governance and Business Processes Management
      1. ABSTRACT
      2. INTRODUCTION
      3. 1. THE STRUCTURE OF CORPORATE GOVERNANCE SYSTEM IN TERMS OF BUSINESS INTELLIGENCE
      4. 2. APPLICATION OF BUSINESS INTELLIGENCE IN CORPORATE GOVERNANCE
      5. 3. APPLICATION OF BUSINESS INTELLIGENCE FOR THE ANALYSIS OF AN EXTERNAL CONTENT OF THE COMPANY (BUSINESS PROCESSES OF MARKETING AND SALES)
      6. 4. APPLICATION OF BUSINESS INTELLIGENCE IN THE FORMATION OF PRODUCT RANGE OF A RETAIL NETWORK (BUSINESS PROCESS MERCANTILE LOGISTICS)
      7. CONCLUSION
  10. Compilation of References
  11. About the Contributors