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Testing Modified Confusion Entropy as Split Criterion for Decision Trees Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Combining Random Subspace Approach with smote Oversampling for Imbalanced Data Classification

Author : Pawel Ksieniewicz

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

Following work tries to utilize a hybrid approach of combining Random Subspace method and smote oversampling to solve a problem of imbalanced data classification. Paper contains a proposition of the ensemble diversified using Random Subspace approach, trained with a set oversampled in the context of each reduced subset of features. Algorithm was evaluated on the basis of the computer experiments carried out on the benchmark datasets and three different base classifiers.

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Footnotes
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Metadata
Title
Combining Random Subspace Approach with smote Oversampling for Imbalanced Data Classification
Author
Pawel Ksieniewicz
Copyright Year
2019
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-030-29858-6

Electronic ISBN: 978-3-030-29859-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-29859-3_56

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