Subspace-based dynamic selection for high-dimensional data

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Subspace-based dynamic selection for high-dimensional data

The number of features collected has increased greatly in the past decade, particularly in medicine and life sciences, which brings challenges and opportunities. Making reliable predictions, exploring associations and extracting meaningful information in high-dimensional data are some of the problem...

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Bibliographic Details
Main Author:	Maciel-Guerra, Alexandre
Format:	Thesis (University of Nottingham only)
Language:	English
Published:	2022
Subjects:	dynamic selection; multiple classifier system; subspace clustering; high-dimensional data; datasets
Online Access:	https://eprints.nottingham.ac.uk/71623/

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