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Titel:

An efficiency-driven, correlation-based feature elimination strategy for small datasets

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Rickert, Carolin A.; Henkel, Manuel; Lieleg, Oliver
Abstract:
With big datasets and highly efficient algorithms becoming increasingly available for many problem sets, rapid advancements and recent breakthroughs achieved in the field of machine learning encourage more and more scientific fields to make use of such a computational data analysis. Still, for many research problems, the amount of data available for training a machine learning (ML) model is very limited. An important strategy to combat the problems arising from data sparsity is feature eliminati...     »
Stichworte:
Data analysis, Data processing, Machine learning, Fluorophores, Chemical bonding, Chemical properties, Antibiotics, Vitamins, Covariance and correlation
Dewey Dezimalklassifikation:
500 Naturwissenschaften
Zeitschriftentitel:
APL Machine Learning
Jahr:
2023
Band / Volume:
1
Heft / Issue:
1
Seitenangaben Beitrag:
016105
Nachgewiesen in:
Scopus
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1063/5.0118207
WWW:
https://pubs.aip.org/aip/aml/article/1/1/016105/2878722/An-efficiency-driven-correlation-based-feature
Verlag / Institution:
AIP Publishing
E-ISSN:
2770-9019
Status:
Verlagsversion / published
Publikationsdatum:
01.03.2023
Semester:
SS 23
TUM Einrichtung:
Fachgebiet für Biomechanik, MW
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