Case-based reasoning : processes, suitability and applications / Antonia M. Leeland, editor.

Format:

Book

Contributor:

Leeland, Antonia M.

Series:

Engineering tools, techniques and tables.

Engineering tools, techniques and tables

Language:

English

Subjects (All):

Case-based reasoning.

Physical Description:

1 online resource (183 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, c2011.

Language Note:

English

Summary:

Case-based reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems. This book presents research in the field of CBR including business predication researches of corporate failure using CBR, and mathematising the Case-Based Reasoning process.

Contents:

Intro

CASE-BASED REASONING: PROCESSES, SUITABILITY AND APPLICATIONS

LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA

CONTENTS

PREFACE

Chapter 1 CASE-BASED REASONING INTEGRATIONS: APPROACHES AND APPLICATIONS

ABSTRACT

1. INTRODUCTION

2. TRENDS IN INTEGRATIONS OF CBR WITH OTHER INTELLIGENT METHODS

3. REPRESENTATIVE SYSTEMS

3.1 Sequential Processing Approaches

3.1.1 Loosely coupled sequence

3.1.2 Tightly coupled sequence

3.2. Co-processing Approaches

3.2.1 Cooperation oriented

3.2.1.1 Explicit reasoning control

3.2.1.2 Implicit reasoning control

3.2.2 Reconciliation oriented

3.3 Embedded Processing

4. COMBINATION OF CBR WITH NEURULES

4.1 Syntax and Semantics

4.2 Indexing and Hybrid Inference

CONCLUSIONS

REFERENCES

Chapter 2 APPLYING IMPROVED CASE INDEXING AND RETRIEVING USING EX-POST INFORMATION IN CORPORATE BANKRUPTCY PREDICTION

INTRODUCTION

LITERATURE REVIEW

Distance Metric

1. Linear distance metrics

2. Value difference metric

Feature selection &amp

determining number of cases

Weighting features

PROPOSED MODEL

RESEARCH DATA AND EXPERIMENTS

Step 1. Selecting Observation Firm Set

Step 2. Categorizing Financial Dimensions

Step 3. Identifying and Obtaining Candidate Financial Ratios

Step 4. Selecting Final Financial Ratios

Step 5. Calculating Efficiencies Using DEA for the Data Set

Step 6. Determining Case Base

Step 7. Dividing Experiment Sets

Step 8. Calculating Feature Weights

Step 9. Measuring Similarity

Step 10. Updating Case Base

RESULT AND ANALYSIS

Experiment 1

Experiment 2

Unsupervised vs. Supervised

CONCLUSION

Chapter 3 CASE-BASED REASONING: HISTORY, METHODOLOGY AND DEVELOPMENT TRENDS

ABSTRACT.

INTRODUCTION

HISTORY OF CBR

THE STEPS OF THE CBR PROCESS

MAIN TYPES OF CBR METHODS

Case-Based Reasoning

Analogy-Based Reasoning

Exemplar-Based Reasoning

Instance-Based Reasoning

Memory-Based Reasoning

5. TOOLS AND APPLICATIONS OF CBR

6. DEVELOPMENT TRENDS OF CBR METHODS AND APPLICATIONS

Chapter 4 A TEMPORAL CASE-BASED PROCEDURE FOR CANCELLATION FORECASTING: A CASE STUDY

2. CANCELLATION CURVES

2.1. Canceling Patterns before Departure

2.2. Cancellation Patterns at Departure

3. MODELS

3.1. Case-Based Predicting Model (CBP)

3.1.1. Similarity evaluation

3.1.2. Sample selection

3.1.3. Prediction generation

3.1.4. Parameter search

3.2. Regression Models

3.3. Pick up Models

4. EMPIRICAL STUDY

4.1. The Best Number of Selection

4.2. Comparison with a Naïve CBP Variant

4.3. Comparison with Four Benchmarks

4.4. Distributions of the Estimated Parameters

ACKNOWLEDGMENT

Chapter 5 PROVISION OF SAFETY FOR TECHNOLOGICAL SYSTEMS WITH THE AID OF CASE-BASED REASONING

2. CONCEPTUALIZATION OF DATA AND KNOWLEDGE

3. THE CASE-BASED APPROACH

4. IMPLEMENTATION OF THE SOFTWARE

5. EXAMPLE OF APPLICATION OF THE SOFTWARE

Chapter 6 MATHEMATIZING THE CASE-BASED REASONING PROCESS

THE MARKOV MODEL

MEASURING THE EFFECTIVENESS OF A CBR SYSTEM

FUZZY SETS

A FUZZY MODEL FOR THE REPRESENTATION OF A CBR SYSTEM

AN APPLICATION OF THE FUZZY MODEL

Chapter 7 PROTOTYPE-BASED REASONING FOR DIAGNOSIS OF DYSMORPHIC SYNDROMES

1.1. Diagnostic Support for Dysmorphic Syndromes

1.2. Other Systems.

1.3. Case-Based Reasoning and Prototypicality Measures

2. DIAGNOSIS OF DYSMORPHIC SYNDROMES

2.1. Prototypicality Measures

2.2. Adaptation Rules

3. RESULTS

3.1. Application of Adaptation Rules

3.2. Application of Adaptation Rules

3.3. Application of Automatically Acquired Adaptation Rules

4. CONCLUSION

Chapter 8 NEW APPROACH OF CASE-BASED REASONING*

2. NEGOTIATION

3. DESCRIPTION OF OUR APPROACH

3.1. The 3R Model

Retrieve

Reuse

Retain

3.2. Real Estate Negotiation According to the 3R Model

Case

Case base

3.3. The 3R Model Cycle

3.3.1. Retrieve

A) Retrieve 1: Optimal weights search

B) Optimal weights specification

B-1) Initialization of the weights

B-2) Calculation of the similarity distance

B-3) Adjustment of the weights

B-4) Calculation of the optimal weights

B) Retrieve 2: The search for the similar case

A) Computation of the Similarity Distance in Relation to the Target

B) Similar Case Retrieval

3.3.3. Retain

4. MODEL VALIDATION

5. CONCLUSION

Commentary FUZZY SETS IN CASE-BASED REASONING

INDEX.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-61728-814-4

OCLC:

788360710