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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 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN 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 Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. 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For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. 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 applications. 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 methods 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 models. 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 editorial 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 (electronics 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