Coefficient of Variation and Machine Learning Applications: 1st Edition (Hardback) book cover

Coefficient of Variation and Machine Learning Applications

1st Edition

By K. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao

CRC Press

120 pages | 30 B/W Illus.

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Hardback: 9780367273286
pub: 2019-12-06
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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 Coefficient 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

About the Authors

K. Himabindu is alumni of NIT- Warangal, JNTU- Hyderabad, and University of Hyderabad. She has a decade of experience in Data Mining and Machine Learning. She is currently working as a Professor in Vishnu Institute of Technology, Bhimavaram and working on a Big Data project sanctioned by Science and Research Board, Government of India. Teaching is her passion and Educational Data Mining is her current research interest.

M Raghava obtained his M. Tech from Mysore University during 2003. He received his PhD from University of Hyderabad. He started his engineering teaching career in CVR College of Engineering, Hyderabad, during 2003 and successfully handled various courses related to Systems Engineering, Neural Networks, Data Engineering and Linux Internals. Currently he is serving as a Professor in CVR College of Engineering, Hyderabad. His areas of interests include Computer Vision, Regularization Theory, Sparse Representations and Graphical Models.

Nilanjan Dey received his Ph. D. Degree from Jadavpur University, India, in 2015. He is an Assistant Professor in the Department of Information Technology, Techno India College of Technology, Kolkata, W.B., India. He holds an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA and Research Scientist of Laboratory of Applied Mathematical Modeling in Human Physiology, Territorial Organization of- Scientific and Engineering Unions, Bulgaria. Associate Researcher of Laboratoire RIADI, University of Manouba, Tunisia. He is the associated Member of University of Reading, London, UK and Scientific Member of - Politécnica of Porto. His research topic is Medical Imaging, Data mining, Machine learning, Computer Aided Diagnosis, Atherosclerosis etc. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence (IGI Global), US, International Journal of Rough Sets and Data Analysis (IGI Global), US, the International Journal of Synthetic Emotions (IGI Global), US, (Co-EinC) and International Journal of Natural Computing Research

(IGI Global) (Co-EinC), US. Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare (AUSAH), Elsevier, Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC Press and Advances in Geospatial Technologies (AGT) Book Series, (IGI Global), US, Executive Editor of International Journal of Image Mining (IJIM), Inderscience, Associated Editor of IEEE Access and International Journal of Information Technology, Springer. He has 20 books and more than 250 research articles in peer reviewed journals and international conferences. He is the organizing committee member

of several international conferences including ITITS, W4C, ICMIR, FICTA, ICICT etc.

C. Raghavendra Rao, Completed his B. Sc, M. Sc. in Statistics from Andhra University and Osmania University respectively. He obtained his Ph. D. in Statistics and M.Tech (CS & Engineering) from Osmania University. He started his carrier as a lecturer in Statistics at Osmania University in 1984. Since 1986, he has been working in the School of Mathematics and Computer/Information Sciences, University of Hyderabad. Presently he is a Professor in the School of Computer and Information Sciences, University of Hyderabad. His current research interests are Simulation & Modeling, Data Analytics, Rough Sets and Knowledge Discovery.

Dr Rao is a member of the Operation Research Society of India, Indian Mathematical Society, International Association of Engineers, Society for development of statistics, Andhra Pradesh Society for Mathematical Sciences, Indian Society for Probability and Statistics, Society for High Energy Materials, International Rough Set Society, Indian Society for Rough Sets, ACM and also a Fellow of The Institution of Electronics and Telecommunication Engineers, Society for Sciences and Andhra Pradesh Academy of Science. Dr Rao Guided 13 PhDs, 55 M. Tech, 8 M.Phils. Nearly 65 Journal and 85 Proceeding Papers to his credit.

Subject Categories

BISAC Subject Codes/Headings:
COM037000
COMPUTERS / Machine Theory
COM059000
COMPUTERS / Computer Engineering
MAT029000
MATHEMATICS / Probability & Statistics / General
TEC007000
TECHNOLOGY & ENGINEERING / Electrical