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Improving Classifier Generalization: Real-Time Machine Learning based Applications (Studies in Computational Intelligence, nr. 989)

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en Limba Engleză Hardback – 30 Sep 2022
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. 

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Specificații

ISBN-13: 9789811950728
ISBN-10: 9811950725
Ilustrații: XXIII, 166 p. 51 illus., 45 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția: 1st ed. 2023
Editura: Springer Nature Singapore
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării: Singapore, Singapore

Cuprins

Chapter 1. Introduction to classification algorithms
a. Basics
b. Bias-variance tradeoff
c. Generalization and sampling error
References

Chapter 2. Methods to improve generalization performance
a. Statistical Learning Theory
i. Vapnik-Chervonenkis Dimension
ii. Growth function
b. Maximum margin classifiers
c. Having fewer parameters - Occam's razor
d. Regularization
e. Boosting
i. Gradient Boosting
f. Transfer learning
g. Unsupervised greedy layerwise learning of deep networks
h. Dropout
i. Conclusions
References

Chapter 3. MVPC – a classifier with very low VC dimension
a. Majority Vote Point classifier
b. Implementation of MVPC
c. Evaluating and comparing VC dimension
i. Upper bound on VC dimension
ii. Empirical estimation of VC dimension with search space reduction
iii. Comparing with linear classifiers
d. Case study on Time-series classification
e. Case study on Gene-expression data classification
f. Conclusions
References

Chapter 4. Framework for reliable fault detection with sensor data
a. Data acquisition framework to simulate real time environment
b. Data Pre-processing
i. Normalization robust to outliers
c. Feature Extraction
d. Feature Selection algorithm using Graphical Indices
i. Feature Ranking and Graphical Indices
ii. Dataset Rejection
iii. Dataset Retrieval
iv. Feature Selection Architecture
e. Classification
f. Sensitive Position Analysis (SPA)
i. Parameter range identification (PRI)
g. Case Study on Air Compressor Fault Detection
i. Leakage Inlet Valve (LIV) fault detection
ii. Leakage Outlet Valve (LOV) fault detection
iii. Online Testing
iv. Real time testing
h. Tutorial for time-series classification
i. Conclusions
References

Chapter 5. Membership functions for Fuzzy Support Vector Machine in noisy environment
a. Fuzzy Support Vector Machine (FSVM)
i. Available Membership Functions for FSVM
ii. Limitations of earlier Membership Functions
iii. Convex Hulls analysis on FSVM
b. Set Measures - Distance Measure between Points and Non-Empty Sets
i. Distance between a Point and a Non Empty Set
ii. Hausdorf Distance (HD)
c. Proposed General Purpose Membership Functions
i. GPMFs with density based clustering
ii. Proposed GPMFs with Fuzzy C-Means clustering
d. Case Study on datasets from UCI repository
i. Class Imbalance Learning in FSVM
ii. Performance Evaluation
iii. Experimentation
e. Results and Analysis
i. Statistical Analysis
f. Conclusions
References

Chapter 6. Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiers
a. Stacked Autoencoder (SAE) based FRC
i. Autoencoders and denoising autoencoders
ii. Weight initialization with unsupervised greedy layerwise learning
iii. Naive stacked autoencoder based FRC
b. Data pre-processing Strategies for SDSAE based FRC
i. Pre-processing Strategy I (PP-I)
ii. Gaussian Mixture Model (GMM) for Pre-Processing
iii. Pre-processing Strategy II
iv. Pre-processing Strategy III (PP-III)
v. Preprocessing of Nominal Features
c. Fine Tuning of weights for FRC Modeling
i. Process Definitions
ii. Fine Tuning Strategy I (FT-I)
iii. Fine Tuning Strategy II (FT-II)
iv. Fine Tuning Strategy III (FT-III)
d. Integration with Expert Knowledge
e. Case study on datasets from UCI repository
i. Dataset wise Observations
f. Conclusions
References

Chapter 7. Epilogue
a. Transfer learning for Molecular Cancer Classification
b. Transfer learning for time-series classification
c. Directions for future work
d. Conclusions
References


Notă biografică

Dr Rahul Sevakula 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.


Textul de pe ultima copertă

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. 


Caracteristici

Includes a special chapter on methods to improve generalization performance during classification
Case studies provide a "how to" for improving classification performance on numerous types of problems
Includes step by step tutorial on how to approach and solve condition monitoring problems