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Pregled bibliografske jedinice broj: 1029785

Feature selection in biomedical signal classification process and current software implementations


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


Naslov
Feature selection in biomedical signal classification process and current software implementations

Autori
Jović, Alan

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Intelligent Decision Support Systems: Applications in Signal Processing

Urednik/ci
Borra, Surekha ; Dey, Nilanjan ; Bhattacharyya, Siddhartha ; Bouhlel, Mohamed Salim

Izdavač
De Gruyter

Grad
Berlin/Boston

Godina
2019

Raspon stranica
1-30

ISBN
978-3-11-061868-6

Ključne riječi
Feature selection, biomedical signal classification, biomedical software, deep learning, deep neural network

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-UIP-2014-09-6889 - Programski sustav za paralelnu analizu više heterogenih nizova vremenskih podataka s primjenom u biomedicini (Alan Jović, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb

Autor s matičnim brojem:
Alan Jović, (294214)

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