Coefficient of Variation and Machine Learning Applications

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.

Chapter 1 Introduction to Coef¿cient of Variation 1.1 INTRODUCTION1.2 COEFFICIENT OF VARIATION 1.3 NORMALIZATION 3 1.4 PROPERTIES OF COEFFICIENT OF VARIATION1.5 LIMITATIONS OF COEFFICIENT OF VARIATION 1.6 CV INTERPRETATION1.7 SUMMARY1.8 EXERCISES Chapter 2 CV Computational Strategies 2.1 INTRODUCTION2.2 CV COMPUTATION OF POOLED DATA 2.3 COMPARISON OF CV WITH ENTROPYAND GINI INDEX2.4 CV FOR CATEGORICAL VARIABLES2.5 CVCOMPUTATIONBYMAP-REDUCESTRATEGIES 2.6 SUMMARY 2.7 EXERCISESChapter 3 Image Representation3.1 INTRODUCTION 3.2 CVIMAGE 3.3 CV FEATURE VECTOR 3.4 SUMMARY 3.5 EXERCISES Chapter 4 Supervised Learning4.1 INTRODUCTION4.2 PRE-PROCESSING (DECISION ATTRIBUTE CALIBRATION)4.3 CONDITIONAL CV4.4 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 EXERCISESChapter 5 Applications5.1 IMAGE CLUSTERING5.2 IMAGE SEGMENTATION5.3 FEATURE SELECTION5.4 MOOD ANALYSIS 5.5 CV FOR OPTIMIZATION5.6 HEALTH CARE 5.7 SOCIAL NETWORK 5.8 SUMMARY 5.9 EXERCISES