A New Evaluation Measure for Imbalanced Datasets

The area of imbalanced datasets is still relatively new, and it is known that the use of overall accuracy is not an appropriate evaluation measure for imbalanced datasets, because of the dominating eect of the majority class. Although, researchers have tried other existing measurements, but there is still no single evaluation measure that work well with imbalanced dataset. In this paper, we introduce a novel measure as a better alternative for evaluating imbalanced dataset. We provide a theoretical background for the new evaluation technique that is designed to cope with cost biases, which changes the previous view about class independent evaluation methods cannot deal with costs, such as ROC curves. We also provide a general guideline for the ideal baseline performance when building classiers with a known misclassication cost.

• Type: Conference
• Year: 2008
• Where: AUSDM
• Authors: Cheng G. Weng, Josiah Poon
• Keywords: Appropriate Evaluation Measure, AUSDM 2008, Data Mining, Evaluation Measure, Imbalanced Dataset