Assessment Metrics for Imbalanced Learning
Nathalie Japkowicz · 2013
This chapter focuses on the aspect of evaluation that concerns the choice of an assessment metric. It concentrates mainly on describing both metrics and graphical methods used in the case of class imbalances, concentrating on well-established methods and pointing out the newer experimental ones. The chapter presents an overview of the three families of assessment metrics used in machine learning - threshold metrics, ranking methods and metrics and probabilistic metrics. It further discusses their general appropriateness to class imbalance situations. The chapter also focuses on the threshold metrics particularly suited for imbalanced datasets. It talks about ranking methods and metrics often used in class-imbalanced situations. The chapter ends by considering other aspects of the classifier evaluation process that could be impacted on by class imbalances, and at the case of multi-class-imbalanced problems.