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Intelligent Decision Support Systems

Applications in Signal Processing

Ed. by Borra, Surekha / Dey, Nilanjan / Bhattacharyya, Siddhartha / Bouhlel, Mohamed Salim

Series:Frontiers in Computational Intelligence 4

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Publication Date:
October 2019
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1. Feature selection in biomedical signal classification process and current software implementations

Jovic, Alan


Feature selection is an important step in everyday data mining. Its aim is to reduce the number of potentially irrelevant expert features describing a dataset to a number of important ones. Unlike feature reduction and transformation techniques, feature selection keeps a subset of the original features, thus maintaining the interpretability of the final models, which is especially important for researchers and medical professionals in the field of biomedicine. The aim of this chapter is to provide an in-depth overview of the various feature selection approaches that are applicable to biomedical signal classification, including: filters, wrappers, embedded methods, and various hybrid approaches. In addition, the recently developed methods based on sequential feature selection and data filtering from streams are considered. Feature selection implementations in current software solutions are described. A comparison of feature selection with deep learning approach is provided. The feature selection approach used in our own web-based biomedical signal analysis platform called MULTISAB (multiple time series analysis in biomedicine) is presented.

Citation Information

Alan Jovic (2019). 1. Feature selection in biomedical signal classification process and current software implementations. In Surekha Borra, Nilanjan Dey, Siddhartha Bhattacharyya, Mohamed Salim Bouhlel (Eds.), Intelligent Decision Support Systems: Applications in Signal Processing (pp. 1–30). Berlin, Boston: De Gruyter. https://doi.org/10.1515/9783110621105-001

Book DOI: https://doi.org/10.1515/9783110621105

Online ISBN: 9783110621105

© 2019 Walter de Gruyter GmbH, Berlin/Munich/BostonGet Permission

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