Soft Computing Applications in Business (Studies in Fuzziness and Soft Computing, 230)

✍ Scribed by Prasad

• Publisher: Springer
• Year: 2008
• Tongue: English
• Leaves: 296
• Category: Library

✦ Synopsis

Soft computing techniques are widely used in most businesses. This book consists of several important papers on the applications of soft computing techniques for the business field. The soft computing techniques used in this book include (or very closely related to): Bayesian networks, biclustering methods, case-based reasoning, data mining, Dempster-Shafer theory, ensemble learning, evolutionary programming, fuzzy decision trees, hidden Markov models, intelligent agents, k-means clustering, maximum likelihood Hebbian learning, neural networks, opportunistic scheduling, probability distributions combined with Monte Carlo methods, rough sets, self organizing maps, support vector machines, uncertain reasoning, other statistical and machine learning techniques, and combinations of these techniques.

The businesses or business problems addressed in this book include (or very closely related to): analysis of correlations between currency exchange rates, analysis of USA banks and Moody’s bank financial strength rating, arrears management, business risk identification, company audit fee evaluation, dental treatments, business internal control, intelligent tutoring systems and educational assessment, modeling agent behavior, motor insurance industry, personal loan defaults, pricing strategies for increasing the market share, pricing strategies in supply chain management, probabilistic sales forecasting, user relevance feedback analysis for online text retrieval, and world crude oil spot price forecasting.

✦ Table of Contents

Title Page
Preface
Contents
Ensembles of Classifiers in Arrears Management
Introduction
Arrears Management
Classifiers and Ensembles
Diversity Measures
Ensemble Construction and Combination Methods
The Application Area and Classification Approaches
The Application Area
Classification Approaches
Experimental Results and Discussion
Concluding Remarks
References
Bicluster Analysis of Currency Exchange Rates
Introduction
Bicluster Patterns
Geometric Biclustering Algorithm
The Fast Hough Transform for Line Detection
The Expansion Algorithm
Experiment Results
Results on the Simplified Difference Dataset
Results on Real Difference Dataset
Conclusion
References
Predicting the Effects of Alternative Pricing Strategies in an Artificial Society Undergoing Technology Adoptio
Technology Markets with Network Externalities
Methods
Consumers as Agents in a Cellular World
Technology Sponsors
Experimental Methods
Results
Incomplete Information
Complete Information
Discussion
References
An Evolutionary Programming Based Knowledge Ensemble Model for Business Risk Identification
Introduction
The EP-Based Knowledge Ensemble Methodology
Brief Introduction of Individual Data Mining Models
Knowledge Ensemble Based on Individual Mining Results
Research Data and Experiment Design
Experiment Results
The Results of Individual Models
Identification Performance of the Knowledge Ensemble
Identification Performance Comparisons
Conclusions
References
The Application of Fuzzy Decision Trees in Company Audit Fee Evaluation: A Sensitivity Analysis
Introduction
Background on Fuzzy Decision Trees
Audit Fees and Fuzzy Decision Tree Analyses
Conclusions
References
An Exposition of NCaRBS: Analysis of US Banks and Moody’s Bank Financial Strength Rating
Introduction
Background on the NCaRBS Technique
The Bank Rating Problem
NCaRBS Analysis of Original BFSR Data Set
NCaRBS Analysis of Incomplete Version of BFSR Data Set
Conclusions
References
A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry
Introduction
Identification of Target Groups in Insurance Business Context
Data Collection, Preprocessing and Exploratory Analysis
Data Collection
Data Preprocessing
Data Exploratory Analysis
Clustering Analysis
Clustering Methods
Clustering Analysis on Motor Insurance Data
Further Business Applications and Discussions
Marketing Campaign
Pricing
Conclusions and Discussions
References
Probabilistic Sales Forecasting for Small and Medium-Size Business Operations
Introduction
Overview of the Business Problem
Business Examples
Revenue Forecasting Tools
Adaptive Processes
A Soft Computing Approach
Monte Carlo Techniques
The Probabilistic Sales Forecasting Model
Defining Base Revenue Distributions
Adjustments to the Base Distributions
Likelihood of Success Estimation and Adjustment
Results
Estimating New Sales
Estimating Partner Sales
Estimating Existing Customer Sales
Aggregated Forecasts
Utilizing Probabilistic Forecasts for Resource Planning
Conclusion
References
User Relevance Feedback Analysis in Text Information Retrieval: A Rough Set Approach
Introduction
Text Information Retrieval
Improving the Performance of Text Information Retrieval
Reasoning Paradigms Used by Text Retrieval Systems
Scope of the Proposed Work
A Review of Current Trends in User Relevance Feedback Analysis
Search Engines
Interactive Information Retrieval Systems
Reasoning Models for Text Retrieval
Discernibility for User Relevance Feedback Analysis
Rough Sets and Discernibility
Using Discernibility to Represent User Preference
Synthesizing Concept Approximation
Designing a Discernibility Based Query Modifier
Results
Modeling Different Perspectives for the Same Domain
Rough Membership Based Document Grading Scheme
Construction of Sieve to Represent User Preference
Grading of New Documents
Designing a User Preference Based Document Filtering System
Results and Discussion
Conclusions
References
Opportunistic Scheduling and Pricing Strategies for Automated Contracting in Supply Chains
Introduction
Responsive Supply Chains
Related Work
Scheduling Strategies
Utility of Scheduling a Task
Pricing Strategies
Experimental Framework
Experimental Results
Experiments with Different Scheduling Strategies
Experiments with Different Pricing Strategies
Conclusion and Future Work
References
Soft Computing in Intelligent Tutoring Systems and Educational Assessment
Introduction
Why Are Intelligent Tutoring Systems Important?
Prior Work on Intelligent Tutoring Systems
Related Research
Short-Answer Free-Text Response Scoring
Paraphrasing and Entailment
Robust Semantic Analysis in Intelligent Tutoring
The Necessity of Finer Grained Analysis
Creating a Training Corpus
The Answer Assessment Technology
Results
Discussion and Future Work
Conclusion
References
A Decision Making System for the Treatment of Dental Caries
Introduction
Problem Definition
Overview
Related Work
System Design
Dental Caries
Inference Mechanism
Bayesian Network of CaDiP
Implementation
Evaluation
Conclusions
References
CBR Based Engine for Business Internal Control
Introduction
Internal Control
Case-Based Reasoning for Internal Control
Maximum Likelihood Hebbian Learning Based Method
Principal Component Analysis (PCA)
${\varepsilon}$-Insensitive Hebbian Learning
Applying Maximum Likelihood Hebbian Learning
Sphering of the Data $g$
Phases of the Proposed System
Results
Conclusions
References
An EMD-Based Neural Network Ensemble Learning Model for World Crude Oil Spot Price Forecasting
Introduction
Methodology Formulation Process
Empirical Mode Decomposition (EMD)
Overall Process of the EMD-Based Neural Network Ensemble Methodology
Experiments
Research Data and Evaluation Criteria
Experimental Results
Conclusions
References
Structured Hidden Markov Models: A General Tool for Modeling Agent Behaviors
Introduction
Structured HMMs
Structure of a Block
Estimating Probabilities in S-HMM
S-HMMs Are Locally Trainable
Modeling Motifs and Duration
Modeling Motifs
Modeling Duration and Gaps
Sequence Segmentation
Knowledge Transfer
Matching Complexity
S-HMM Construction and Training
Discovery Algorithm
Locally Training Nested Blocks
Modeling Keystroking Dynamics of a Human Agent
Input Information
Modeling User Behavior
Model Construction
User Profiling
User Authentication
Conclusions
References
Author Index