2020
DOI: 10.48550/arxiv.2004.01764
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods

Abstract: This study uses stacked generalization, which is a two-step process of combining machine learning methods, called meta or super learners, for improving the performance of algorithms in step one (by minimizing the error rate of each individual algorithm to reduce its bias in the learning set) and then in step two inputting the results into the meta learner with its stacked blended output (demonstrating improved performance with the weakest algorithms learning better). The method is essentially an enhanced cross… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 32 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?