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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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2019, 2019
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| Edition: | 1st ed. 2019 |
| Series: | Springer Theses, Recognizing Outstanding Ph.D. Research
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| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | XV, 107 p. 35 illus., 32 illus. in color online resource |
| ISBN: | 9783319986753 |