Improving classifier generalization: real-time machine learning based applications
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 monitori…
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- Full title :Improving classifier generalization : real-time machine learning based applications / Rahul Kumar Sevakula, Nishchal K. Verma.
Summary
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. .
Read More ↓Edition Statements
- Publication Date:2023
Publication Statements
- Publisher(s):Springer
- Place of Publication:Singapore
- Language:eng
- Publication Date:2023
The Physical Object
- Description:1 online resource : : illustrations (black and white, and color).
Edition Identifiers
- ISBN:9789811950735, 9811950733
- DOI:10.1007/978-981-19-5073-5
- oclc:on1347262296
- LC Classification:Q325.5
- Dewey Decimal:006.3/1
Table of Contents
Introduction to classification algorithms -- Methods to improve generalization performance -- MVPC a classifier with very low VC dimension -- Framework for reliable fault detection with sensor data -- Membership functions for Fuzzy Support Vector Machine in noisy environment -- Stacked Denoising Sparse Autoencoder based Fuzzy rule classifiers -- Epilogue.
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