Abstract

Unbalanced data, a snag often found in real-world applications, can seriously adversely affect machine learning algorithms ' classification efficiency. Various tries are made to classify unbalanced data sets. In order to face the imbalanced data sets snag, we should rebalance them artificially through machine learning classifiers by oversampling and/or under-sampling.

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