Jul 31, 2019 · Our paper presents two case studies, each utilizing a unique, combined approach of Random Undersampling and Feature Selection to investigate the effect of ...
We conclude that combining Feature Selection with Random Undersampling improves the classification performance of learners with imbalanced big data from ...
Jul 17, 2019 · We conclude that combining Feature Selection with Random. Undersampling improves the classification performance of learners with imbalanced big ...
It is concluded that combining Feature Selection with Random Undersampling improves the classification performance of learners with imbalanced big data from ...
Abstract: High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias ...
Jul 17, 2019 · We conclude that combining Feature Selection with Random Undersampling improves the classification performance of learners with imbalanced big ...
High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias predictions in favor ...
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Katal A, Wazid M, Goudar R. Big data: issues, challenges, tools and good practices. In: 2013 sixth international conference on contemporary computing (IC3).
Nov 7, 2023 · In this article, you will learn how to balance imbalanced data when evaluating a predictive model, and why it is important to do so.
Examining characteristics of predictive models with imbalanced big data ... In their 2018 study on data sampling and imbalanced big data, Bauder et al.