Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

149 Pages·2022·2.919 MB·English

by  Hemachandran K, Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose

Bayesian Reasoning
and Gaussian P rocesses
for Machine Learning
Applications

Bayesian Reasoning
and Gaussian P rocesses
for Machine Learning
Applications
Edited by
Hemachandran K
Shubham Tayal
Preetha Mary George
Parveen Singla
Utku Kose
First edition published 2022
by CRC Press
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and by CRC Press
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CRC Press is an imprint of Taylor & Francis Group, LLC
© 2022 selection and editorial matter, Hemachandran K, Shubham Tayal, Preetha Mary George, Praveen Singla and
Utku Kose; individual chapters, the contributors
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Library of Congress Cataloging-in-Publication Data
Names: K., Hemachandran, editor. | Tayal, Shubham, editor. | George,
Preetha Mary, editor. | Singla, Parveen, editor. | Kose, Utku, 1985- editor.
Title: Bayesian reasoning and Gaussian processes for machine learning
applications / edited by Hemachandran K., Shubham Tayal, Preetha Mary
George, Parveen Singla, Utku Kose.
Description: First edition. | Boca Raton : Chapman & Hall/CRC Press, 2022. |
Includes bibliographical references and index. | Summary: “The book Bayesian Reasoning and Gaussian Processes for
Machine Learning Applications talks about Bayesian Reasoning and Gaussian Processes in machine learning applica-
tions. Bayesian methods are applied in many areas such as game development, decision making and drug discovery. It
is very effective for machine learning algorithms for handling missing data and for extracting information from small
datasets. This book introduces a statistical background which is needed to understand continuous distributions and
it gives an understanding on how learning can be viewed from a probabilistic framework. The chapters of the book
progress into machine learning topics such as Belief Network, Bayesian Reinforcement Learning etc., which is fol-
lowed by Gaussian Process Introduction, Classification, Regression, Covariance and Performance Analysis of GP with
other models. This book is aimed primarily at graduates, researchers and professionals in the field of data science and
machine learning”—Provided by publisher.
Identifiers: LCCN 2021052540 (print) | LCCN 2021052541 (ebook) |
ISBN 9780367758479 (hardback) | ISBN 9780367758493 (paperback) |
ISBN 9781003164265 (ebook)
Subjects: LCSH: Bayesian statistical decision theory—Data processing. |
Gaussian processes—Data processing. | Machine learning.
Classification: LCC QA279.5 .B43 2022 (print) | LCC QA279.5 (ebook) |
DDC 006.3/101519542—dc23/eng/20220128
LC record available at https://lccn.loc.gov/2021052540
LC ebook record available at https://lccn.loc.gov/2021052541
ISBN: 978-0-367-75847-9 (hbk)
ISBN: 978-0-367-75849-3 (pbk)
ISBN: 978-1-003-16426-5 (ebk)
DOI: 10.1201/9781003164265
Typeset in Palatino
by codeMantra
Contents
Preface ............................................................................................................................................vii
Editors ..............................................................................................................................................ix
Contributors .................................................................................................................................xiii
1. Introduction to Naive Bayes and a Review on Its Subtypes with Applications .......1
Eguturi Manjith Kumar Reddy, Akash Gurrala, Vasireddy Bindu Hasitha, and
Korupalli V Rajesh Kumar
2. A Review on the Different Regression Analysis in Supervised Learning ..............15
K Sudhaman, Mahesh Akuthota, and Sandip Kumar Chaurasiya
3. Methods to Predict the Performance Analysis of Various Machine
Learning Algorithms ...........................................................................................................33
M. Saritha, M. Lavanya, and M. Narendra Reddy
4. A Viewpoint on Belief Networks and Their Applications ..........................................49
G S Sivakumar, P Suneetha, V Sailaja, and Pokala Pranay Kumar
5. Reinforcement Learning Using Bayesian Algorithms with Applications ...............57
H. Raghupathi, G Ravi, and Rajan Maduri
6. Alerting System for Gas Leakage in Pipelines ..............................................................63
Nilesh Deotale, Pragya Chandra, Prathamesh Dherange, Pratiksha Repaswal, and
Saibaba V More
7. Two New Nonparametric Models for Biological Networks ........................................77
Deniz Seçilmiş, Melih Ağraz, and Vilda Purutçuoğlu
8. Generating Various Types of Graphical Models via MARS .....................................101
E. Ayyıldız and V. Purutçuoğlu
9. Financial Applications of Gaussian Processes and Bayesian Optimization .........111
Syed Hasan Jafar
10. Bayesian Network Inference on Diabetes Risk Prediction Data ..............................123
M. Ö. Cingiz
Index .............................................................................................................................................133
v

Preface
When we look into the past years, we can see an explosion in the applications of machine
learning, particularly in e-commerce, social media, gaming, drug discovery, and many
other verticals. These applications were focused on predictive accuracy and involved huge
amounts of data. Bayesian methods give superpowers to machine learning algorithms, in
handling missing data and in extracting information from small data sets. Bayesian meth-
ods help estimate uncertainty in predictions, which enhances the field of medicine. They
allow to compress models a hundredfold and to automatically tune hyperparameters by
saving time and money. In Bayesian Reasoning and Gaussian Processes for Machine Learning
Applications, we discuss the basics of Bayesian methods, define probabilistic models, and
make predictions using them. We discuss the automated workflow and some advanced
techniques on how to speed up the process. We also look into the applications of Bayesian
methods in deep learning and to generate images.
This book is designed to encourage researchers and students from multiple disciplines
toward the arena of applications of machine learning. It aims to introduce a statistical
background needed to understand continuous distributions and how learning can be
viewed from a probabilistic framework. It also discusses machine learning topics such
as belief network, Bayesian reinforcement learning, Gaussian process with classification,
regression, covariance, and performance analysis of Gaussian processes with other mod-
els. This book is segmented into ten chapters.
Chapter 1 deals with the introduction of Naive Bayes – a collection of algorithms based
on Bayes theorem – and its applications. It’s a simple technique for constructing classifiers.
Chapter 2 gives insights on different regression analyses in supervised learning. Chapter 3
throws light on different methods to predict the performance analysis of various machine
learning applications. Chapter 4 discusses on belief networks and its applications. Chapter 5
describes reinforcement learning using Bayesian algorithms with applications. Chapter 6
intuits on alerting system for gas leakage in pipelines. Chapter 7 gives a perception on
non-parametric models for biological networks. Chapter 8 provides a deep understanding
on generating various types of graphical models via MARS. Chapter 9 is an acumen on
financial applications of Gaussian processes and Bayesian optimization. Chapter 10 gives a
panoramic view on Bayesian Network interface on diabetes risk prediction data. The book
gives an insight into how new drugs can cure severe diseases with Bayesian methods.
We hope our attempt in this book will be beneficial for the student community, indus-
trialists, researchers, their mentors, and to all people who wish to explore the applications
of machine learning. We are greatly thankful to our contributors who hail from renowned
institutes and industries that made a remarkable contribution by imparting their knowl-
edge for the welfare of society. We express our sincere, wholehearted thanks to our edito-
rial and production teams for their relentless contribution and for rendering unconditional
support to publish this book on time.
vii

Editors
Hemachandran K has been a passionate teacher for
14 years, with 5 years of research experience. He is a strong
educational professional with a flair for science, highly
skilled in artificial intelligence and machine learning.
After earning a PhD in embedded systems at Dr. M.G.R.
Educational and Research Institute, India, he started con-
ducting interdisciplinary research in artificial intelligence.
He is an open-minded and positive person who has stu-
pendous peer-reviewed publication records with more than
20 journals and international conference publications. He
served as an effective resource person at various national
and international scientific conferences. He has a rich research experience in mentoring
undergraduate and postgraduate projects. He holds two patents to his credentials. He has
life membership in esteemed professional institutions. He was a pioneer in establish Single
Board Computer lab at Ashoka Institutions, Hyderabad, India. Because of his self-paced
learning schedule and thirst for upgrading and updating learning skills, he was awarded
around 15 online certificate courses conferred by COURSERA and other online platforms.
His editorial skills led him to be included as an editorial board member for numerous
reputed SCOPUS/SCI journals.
Shubham Tayal is Assistant Professor in the Department
of Electronics and Communication Engineering at SR
University, Warangal, Telangana, India. He has more than
6 years of academic/research experience of teaching at the
UG and PG levels. He earned a PhD in microelectronics
and VLSI design at the National Institute of Technology,
Kurukshetra; an MTech (VLSI design) at YMCA University
of Science and Technology, Faridabad; and a BTech (elec-
tronics and communication engineering) at MDU, Rohtak.
He has published more than 25 research papers in various
international journals and conferences of repute, and many
papers are under review. He is on the editorial and reviewer
panels of many SCI/SCOPUS-indexed international journals and conferences. Currently,
he is the editor or coeditor for six books with CRC Press (Taylor & Francis Group, USA).
He acted as a keynote speaker and delivered professional talks on various forums. He is
a member of various professional institutions such as IEEE, IRED, etc. He is on the advi-
sory panel of many international conferences. He is a recipient of the Green ThinkerZ
International Distinguished Young Researcher Award 2020. His research interests include
simulation and modeling of multi-gate semiconductor devices, device-circuit codesign in
digital/analogue domain, machine learning, and IoT.
ix