Sevakula / Verma Improving Classifier Generalization

Real-Time Machine Learning based Applications
1. Auflage 2023
ISBN: 978-981-19-5073-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 166 Seiten, eBook

Reihe: Engineering

This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. 

Zielgruppe

Research

Autoren/Hrsg.

Sevakula, Rahul Kumar

Verma, Nishchal K.

Inhaltsverzeichnis

Chapter 1. Introduction to classification algorithmsa. Basicsb. Bias-variance tradeoffc. Generalization and sampling errorReferences
Chapter 2. Methods to improve generalization performancea. Statistical Learning Theoryi. Vapnik-Chervonenkis Dimensionii. Growth functionb. Maximum margin classifiersc. Having fewer parameters - Occam's razord. Regularizatione. Boostingi. Gradient Boostingf. Transfer learningg. Unsupervised greedy layerwise learning of deep networksh. Dropouti. ConclusionsReferences
Chapter 3. MVPC – a classifier with very low VC dimensiona. Majority Vote Point classifierb. Implementation of MVPCc. Evaluating and comparing VC dimensioni. Upper bound on VC dimensionii. Empirical estimation of VC dimension with search space reductioniii. Comparing with linear classifiersd. Case study on Time-series classificatione. Case study on Gene-expression data classificationf. ConclusionsReferences
Chapter 4. Framework for reliable fault detection with sensor dataa. Data acquisition framework to simulate real time environmentb. Data Pre-processingi. Normalization robust to outliersc. Feature Extractiond. Feature Selection algorithm using Graphical Indicesi. Feature Ranking and Graphical Indicesii. Dataset Rejectioniii. Dataset Retrievaliv. Feature Selection Architecturee. Classificationf. Sensitive Position Analysis (SPA)i. Parameter range identification (PRI)g. Case Study on Air Compressor Fault Detectioni. Leakage Inlet Valve (LIV) fault detectionii. Leakage Outlet Valve (LOV) fault detectioniii. Online Testingiv. Real time testingh. Tutorial for time-series classificationi. ConclusionsReferences
Chapter 5. Membership functions for Fuzzy Support Vector Machine in noisy environmenta. Fuzzy Support Vector Machine (FSVM)i. Available Membership Functions for FSVMii. Limitations of earlier Membership Functionsiii. Convex Hulls analysis on FSVMb. Set Measures - Distance Measure between Points and Non-Empty Setsi. Distance between a Point and a Non Empty Setii. Hausdorf Distance (HD)c. Proposed General Purpose Membership Functionsi. GPMFs with density based clusteringii. Proposed GPMFs with Fuzzy C-Means clusteringd. Case Study on datasets from UCI repositoryi. Class Imbalance Learning in FSVMii. Performance Evaluationiii. Experimentatione. Results and Analysisi. Statistical Analysisf. ConclusionsReferences
Chapter 6. Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiersa. Stacked Autoencoder (SAE) based FRCi. Autoencoders and denoising autoencodersii. Weight initialization with unsupervised greedy layerwise learningiii. Naive stacked autoencoder based FRCb. Data pre-processing Strategies for SDSAE based FRCi. Pre-processing Strategy I (PP-I)ii. Gaussian Mixture Model (GMM) for Pre-Processingiii. Pre-processing Strategy IIiv. Pre-processing Strategy III (PP-III)v. Preprocessing of Nominal Featuresc. Fine Tuning of weights for FRC Modelingi. Process Definitionsii. Fine Tuning Strategy I (FT-I)iii. Fine Tuning Strategy II (FT-II)iv. Fine Tuning Strategy III (FT-III)d. Integration with Expert Knowledgee. Case study on datasets from UCI repositoryi. Dataset wise Observationsf. ConclusionsReferences
Chapter 7. Epiloguea. Transfer learning for Molecular Cancer Classificationb. Transfer learning for time-series classificationc. Directions for future workd. ConclusionsReferences

Über Autor(innen)

Dr Sevakula Rahul Kumar has over 10 years of research experience in machine learning (ML) and deep learning (DL). He received his Bachelor’s degree from the National Institute of Technology (NIT) Warangal, India in 2009 and later his Ph.D. degree from the Indian Institute of Technology (IIT) Kanpur, India in 2017. He is currently a Sr. Research Scientist at Whoop, and his research interests lie at the intersection of ML, physiological signals, cardiovascular health monitoring (medicine) and wearables. Prior to joining Whoop, he was an Instructor (junior research faculty) at Harvard Medical School and Massachusetts General Hospital, USA, and a Data Scientist at IBM India. He has filed multiple patent disclosures and has published over 45 research papers in international peer-reviewed journals and conferences. He is also a reviewer for several journals of national and international repute.

Dr. Nishchal K. Verma is a Professor in the Department of Electrical Engineering at Indian Institute of Technology (IIT) Kanpur, India.  Dr. Verma's research interest falls in Artificial Intelligence (AI) related theories and its practical applications to inter-disciplinary domains like machine learning, deep learning, computer vision, prognosis and health management, bioinformatics, cyber-physical systems, complex and highly non-linear systems modeling, clustering, and classifications, etc. He has published more than 250 research papers in peer-reviewed reputed conferences and journals along with 4 books (edited/ co-authored) in the field of AI. He has 20+ years of experience in the field of AI. He is currently serving as Associate Editor/ Editorial Board Member of various reputed journals and conferences. He has also developed several AI-related key technologies for The BOEING Company, USA.