Experimental perspectives on learning from imbalanced data

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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

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ICML 2007 | Imbalanced Data | Machine Learning |

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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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Experimental perspectives on learning from imbalanced data

ICML 2007 | Imbalanced Data | Machine Learning |

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