Implementation of an Asymmetric Adjusted Activation Function for Class Imbalance Credit Scoring
This addresses the challenge of accurately classifying minority default cases in financial credit scoring, offering a competitive tool for the industry, though it is incremental as it builds on existing activation functions.
The paper tackled the problem of class imbalance in credit scoring by introducing an asymmetric adjusted activation function (ASIG) that auto-adjusts based on imbalance ratios, resulting in improved performance over traditional classifiers across various imbalance levels with robustness in ultra-high imbalance scenarios.
Credit scoring is a systematic approach to evaluate a borrower's probability of default (PD) on a bank loan. The data associated with such scenarios are characteristically imbalanced, complicating binary classification owing to the often-underestimated cost of misclassification during the classifier's learning process. Considering the high imbalance ratio (IR) of these datasets, we introduce an innovative yet straightforward optimized activation function by incorporating an IR-dependent asymmetric adjusted factor embedded Sigmoid activation function (ASIG). The embedding of ASIG makes the sensitive margin of the Sigmoid function auto-adjustable, depending on the imbalance nature of the datasets distributed, thereby giving the activation function an asymmetric characteristic that prevents the underrepresentation of the minority class (positive samples) during the classifier's learning process. The experimental results show that the ASIG-embedded-classifier outperforms traditional classifiers on datasets across wide-ranging IRs in the downstream credit-scoring task. The algorithm also shows robustness and stability, even when the IR is ultra-high. Therefore, the algorithm provides a competitive alternative in the financial industry, especially in credit scoring, possessing the ability to effectively process highly imbalanced distribution data.