Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
Thuy T. PhamThis 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.
Rok:
2019
Wydanie:
1st ed.
Wydawnictwo:
Springer International Publishing
Język:
english
ISBN 10:
3319986759
ISBN 13:
9783319986753
Serie:
Springer Theses
Plik:
PDF, 4.45 MB
IPFS:
,
english, 2019