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Chapter 1: Bayesian Networks as a Decision Support Tool in Credit Scoring Domain
Table 1: Probability table P(<Customer Category>)
Table 2: Probability table P(<Customer Category >|<Debit>)
Table 3: Stages of credit granting process
Table 4: Areas of enterprise analysis
Table 5: The scoring table for the "profitability" area (normative point scale)
Table 6: The scoring tables for the rest of the objective factors areas
Table 7: Subjective factors, scoring tables
Table 8: The score corresponding to the class of the economic-financial position
Table 9: The score corresponding to the class of the economic-financial position and the previous credit performance
Table 10: Example-The score corresponding to factors
Table 11: The total score corresponding to qualitative and quantitative factors
Table 12: Conditional probability distributions in "quality of management" node
Table 13: The factors considered in Bayesian network structure of credit scoring system
Chapter 3: An Evolutionary Misclassification Cost Minimization Approach for Medical Diagnosis
Table 1: Results of tests of difference in means for training and holdout samples
Table 2: Results of tests in training and holdout samples of heart disease data
Table 3: Results for tests in training and holdout samples for BUPA data set
Chapter 4: Guiding Knowledge Discovery Through Interactive Data Mining
Table 1: Textual presentation of clustering algorithm results
Table 2: Layered interaction model (Nielson, 1992)
Chapter 5: A Proposed Process for Performing Data Mining Projects
Table 1: Mapping observed activities to straw man
Chapter 6: Data Mining for Optimal Combination Demand Forecasts
Table 1: Summary statistics of the 26 items
Table 2: The alternative forecasting methods in each procedure
Table 3a: Forecasting results for sample A of 8 items
Table 3b: Forecasting results for sample B of 2 items
Table 4a: Results for common forecasting methods for sample A items
Table 4b: Results for common forecasting methods for sample B items
Table 5: F-statistics and comparisons of forecast error variances
Table 6: Results for common weights and forecasting method
Table 7: Summary of results
Table 8: A comparison of ARl and seasonal AR1 for the 10 items
Table 9: Summary of results if Holt-Winters method replaces Holt's method
Chapter 9: Utilization of Data Mining Techniques to Detect and Predict Accounting Fraud: A Comparison of Neural Networks and Discriminant Analysis
Table 1: Predicted group membership
Table 2: Percent good parts of neural net testing
Chapter 12: Combination Forecasts Based on Markov Chain Monte Carlo Estimation of the Mode
Table 1: Results for case 1 simulation study
Table 2: Results for case 2 simulation study
Table 3: Autocorrelation coefficients of arrival series
Table 4: Results of individual forecast method
Table 5: Results of combination forecast methods
Table 6: RMSE values of all the forecasting methods
Chapter 14: Connectionist and Evolutionary Models for Learning, Discovering and Forecasting Software Effort
Table 1: A few major software project failures in US and in UK
Table 2: Performance of three different structural designs during the training phase
Table 3: Performance of three different networks during the test phase
Table 4: Pair-wise comparison (t-test) results for difference in means for actual and predicted software effort
Table 5: Pair-wise comparison (t-test) results for difference in means for actual and predicted software effort
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Managing Data Mining Technologies in Organizations: Techniques and Applications
ISBN: 1591400570
EAN: 2147483647
Year: 2003
Pages: 174
Authors:
Parag Pendharkar
BUY ON AMAZON
The CISSP and CAP Prep Guide: Platinum Edition
Telecommunications and Network Security
Understanding Certification and Accreditation
Continuous Monitoring Process
Appendix A Answers to Assessment Questions
Appendix D The Information System Security Engineering Professional (ISSEP) Certification
Documenting Software Architectures: Views and Beyond
Relation to Other Viewtypes
For Further Reading
One Document or Several?
Module Decomposition View
C&C Pipe-and-Filter View
Building Web Applications with UML (2nd Edition)
What This Book Is About
JavaScript Objects
Custom JavaScript Objects
Design
Advanced Client-Side Scripting
Visual C# 2005 How to Program (2nd Edition)
Class View and Object Browser
Wrap-Up
Terminology
Exercises
F.2. Editing XHTML
Data Structures and Algorithms in Java
Encapsulation
Projects
Vocabulary
Explicit Memory Management
A.9. Debugging
User Interfaces in C#: Windows Forms and Custom Controls
Designing with Classes and Tiers
Forms
Modern Controls
GDI+ Controls
Help and Application-Embedded Support
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