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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Data analysis, Data processing, Machine learning, Fluorophores, Chemical bonding, Chemical properties, Antibiotics, Vitamins, Covariance and correlation
Dewey Decimal Classification:
500 Naturwissenschaften
Journal title:
APL Machine Learning
Year:
2023
Journal volume:
1
Journal issue:
1
Pages contribution:
016105
Covered by:
Scopus
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1063/5.0118207
WWW:
https://pubs.aip.org/aip/aml/article/1/1/016105/2878722/An-efficiency-driven-correlation-based-feature
Publisher:
AIP Publishing
E-ISSN:
2770-9019
Status:
Verlagsversion / published
Date of publication:
01.03.2023
Semester:
SS 23
TUM Institution:
Fachgebiet für Biomechanik, MW
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