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Coefficient of Variation and Machine Learning Applications
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Book Description
Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.
Table of Contents
Chapter 1 Introduction to Coef¿cient of Variation
1.1 INTRODUCTION
1.2 COEFFICIENT OF VARIATION
1.3 NORMALIZATION 3
1.4 PROPERTIES OF COEFFICIENT OF VARIATION
1.5 LIMITATIONS OF COEFFICIENT OF VARIATION
1.6 CV INTERPRETATION
1.7 SUMMARY
1.8 EXERCISES
Chapter 2 CV Computational Strategies
2.1 INTRODUCTION
2.2 CV COMPUTATION OF POOLED DATA
2.3 COMPARISON OF CV WITH ENTROPYAND GINI INDEX
2.4 CV FOR CATEGORICAL VARIABLES
2.5 CVCOMPUTATIONBYMAP-REDUCESTRATEGIES
2.6 SUMMARY
2.7 EXERCISES
Chapter 3 Image Representation
3.1 INTRODUCTION
3.2 CVIMAGE
3.3 CV FEATURE VECTOR
3.4 SUMMARY
3.5 EXERCISES
Chapter 4 Supervised Learning
4.1 INTRODUCTION
4.2 PRE-PROCESSING (DECISION ATTRIBUTE CALIBRATION)
4.3 CONDITIONAL CV
4.4 CVGAIN (CV FOR ATTRIBUTE SELECTION)
4.5 ATTRIBUTE ORDERING WITH CVGAIN
4.6 CVDT FOR CLASSIFICATION
4.7 CVDT FOR REGRESSION
4.8 CVDT FOR BIG DATA
4.9 FUZZY CVDT
4.10 SUMMARY
4.11 EXERCISES
Chapter 5 Applications
5.1 IMAGE CLUSTERING
5.2 IMAGE SEGMENTATION
5.3 FEATURE SELECTION
5.4 MOOD ANALYSIS
5.5 CV FOR OPTIMIZATION
5.6 HEALTH CARE
5.7 SOCIAL NETWORK
5.8 SUMMARY
5.9 EXERCISES