Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/262229 
Year of Publication: 
2021
Citation: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Zagreb, Croatia, 9-10 September 2021 [Publisher:] IRENET - Society for Advancing Innovation and Research in Economy [Place:] Zagreb [Year:] 2021 [Pages:] 1-12
Publisher: 
IRENET - Society for Advancing Innovation and Research in Economy, Zagreb
Abstract: 
The confirmed approach to choosing the number of principal components for prediction models includes exploring the contribution of each principal component to the total variance of the target variable. A combination of possible important principal components can be chosen to explain a big part of the variance in the target. Sometimes several combinations of principal components should be explored to achieve the highest accuracy in classification. This research proposes a novel automatic way of deciding how many principal components should be retained to improve classification accuracy. We do that by combining principal components with the ANOVA selection. To improve the accuracy resulting from our automatic approach, we use the bootstrap procedure for model selection. We call this procedure the Bootstrapped-ANOVA PCA selection. Our results suggest that this procedure can automate the principal components selection and improve the accuracy of classification models, in our example, the logistic regression.
Subjects: 
ANOVA
PCA
Bootstrap
logistic regression
JEL: 
C38
C52
C63
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Conference Paper

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