Improving classifier generalization : real-time machine learning based applications / Rahul Kumar Sevakula, Nishchal K. Verma.

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Improving classifier generalization : real-time machine learning based applications / Rahul Kumar Sevakula, Nishchal K. Verma.

9789811950735 (electronic bk.)
9811950733 (electronic bk.)
9811950725
9789811950728

Singapore : Springer, [2023]

English

1 online resource : illustrations (black and white, and color).

10.1007/978-981-19-5073-5 doi

Q325.5

006.3/1

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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Description based on print version record.

Studies in computational intelligence ; v. 989.

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