Boruta algorithm: An alternative feature selection method in credit scoring model

This paper analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant v...

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Bibliographic Details
Main Authors: Handhika, Tri, Murni, Fahreza, Rafi Mochamad
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Classification
Machine learning
Prediction models
Programming languages
Scoring models
Supervised learning
Online Access:Get full text
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Summary:This paper analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant variables (feature selection) in a classification model in the machine learning approach, as supervised learning. Our results show that on the German Credit Data from the UCI Machine Learning with 20 variables, feature selection using Boruta Algorithm with Python Programming obtains 4 significant features.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0114178