Abstract
In Chap. 2, general technical background of automated classification and feature engineering concepts were discussed. In this chapter, more detailed algorithms of the proposed work are presented. Specifically, three scenarios of automated classification are introduced in the context of a desirable subject-independent settings. There are two separate sections for two functional blocks for a classification task: feature engineering and sorting algorithms. The first block is a common scheme for several scenarios, on the other hand the second block is specific to each application scenario. The first section describes a voting-based feature (data representation) selection process and its criteria. Then the next parts formulate the problem for three classification scenarios: point anomaly detection (Sect. 3.2.1), collective anomaly detection (Sect. 3.2.2), and unsupervised multi-class sorting (Sect. 3.2.3).
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Pham, T.T. (2019). Algorithms. In: Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-98675-3_3
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DOI: https://doi.org/10.1007/978-3-319-98675-3_3
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