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

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, includ...

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Detaylı Bibliyografya
Yazar: Pham, Thuy T. (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edisyon:1st ed. 2019.
Seri Bilgileri:Springer Theses, Recognizing Outstanding Ph.D. Research,
Konular:
Biomedical engineering.
Data mining.
Computational intelligence.
Bioinformatics.
Biomedical Engineering and Bioengineering.
Data Mining and Knowledge Discovery.
Computational Intelligence.
Online Erişim:Full-text access
OPAC'ta görüntüle
Diğer Bilgiler
Özet: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.
Fiziksel Özellikler:XV, 107 p. 35 illus., 32 illus. in color. online resource.
ISBN:9783319986753
ISSN:2190-5061
DOI:10.1007/978-3-319-98675-3

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