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A New Evaluation Measure for Imbalanced Datasets
Weng, C.G. and Poon, J.
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 e ect 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 classi ers with a known misclassi cation
cost.
Cite as: Weng, C.G. and Poon, J. (2008). A New Evaluation Measure for Imbalanced Datasets. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick, J. F., Li, J., Christen, P. and Kennedy, P. J., Eds. ACS. 27-32.
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