English | PDF,EPUB | 2018 (2019 Edition) | 114 Pages | ISBN : 3319986740 | 7.81 MB

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 -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.



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