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ICML
2007
IEEE

Experimental perspectives on learning from imbalanced data

9 years 12 months ago
Experimental perspectives on learning from imbalanced data
We present a comprehensive suite of experimentation on the subject 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 objective 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 Van Hulse, Taghi M. Khoshgoftaar, Amri Napol
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
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