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We propose a novel output layer activation function, which we name ASTra(Asymmetric Sigmoid Transfer function), which makes the classification ofminority examples, in scenarios of high imbalance, more tractable. We combinethis with a loss function that helps to effectively target minoritymisclassification. These two methods can be used together or separately, withtheir combination recommended for the most severely imbalanced cases. Theproposed approach is tested on datasets with IRs from 588.24 to 4000 and veryfew minority examples (in some datasets, as few as five). Results using neuralnetworks with from two to 12 hidden units are demonstrated to be comparable to,or better than, equivalent results obtained in a recent study that deployed awide range of complex, hybrid data-level ensemble classifiers.

ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification