Experimental Persp ectives on Learning from Imbalanced Data

Experimental Persp ectives on Learning from Imbalanced Data
Jason D. Van Hulse - Florida Atlantic University, USA Taghi M. Khoshgoftaar - Florida Atlantic University, USA Amri Napolitano - Florida Atlantic University, USA
We present a comprehensive suite of experimentation on the sub ject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the perspective of reduced performance. Can data sampling be used to improve the performance of learners built from imbalanced data? Is the effectiveness of sampling related to the type of learner? Do the results change if the ob jective is to optimize different performance metrics? We address these and other issues in this work, showing that sampling in many cases will improve classifier performance.

Jason D. Van Hulse - Florida Atlantic University, USA
Taghi M. Khoshgoftaar - Florida Atlantic University, USA
Amri Napolitano - Florida Atlantic University, USA

We present a comprehensive suite of experimentation on the sub ject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the perspective of reduced performance. Can data sampling be used to improve the performance of learners built from imbalanced data? Is the effectiveness of sampling related to the type of learner? Do the results change if the ob jective is to optimize different performance metrics? We address these and other issues in this work, showing that sampling in many cases will improve classifier performance.