Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

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Synopsis

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.

Book details

Series:
Springer Theses
Author:
Thuy T. Pham
ISBN:
9783319986753
Related ISBNs:
9783319986746
Publisher:
Springer International Publishing
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2018-10-22
Usage restrictions:
Copyright
Copyright date:
2019
Copyright by:
Springer Nature Switzerland AG 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Nonfiction, Science, Technology