Estimation of optimum thresholds for binary classification using genetic algorithm: An application to solve a credit scoring problem
Bibliographic record
Abstract
Abstract The main issue in a classification problem is classifying observations into various disjoint classes. Different classification techniques generate a continuous number between a and b, usually between 0 and 1; thus, the optimal cut‐off value(s) must be carefully selected to discriminate classes precisely. The decision is about setting a threshold value and transforming the continuous score into a binary output. Therefore, in addition to using the so‐called sophisticated classification methods to have a more accurate classification, there is a need to identify and choose the optimal threshold value(s). However, the latter has not been thoroughly investigated. Hence, this study proposes an approach based on a Genetic Algorithm (GA) and Neural Networks (NNs) to automatically find customized cut‐off values, considering different performance criteria and given datasets. Since credit scoring is a binary classification problem, two popular credit scoring datasets, namely “Australian” and “German” credit datasets, are used to test the proposed approach. Our numerical results revealed that the proposed GA‐NN model could successfully find customized acceptance thresholds, considering predetermined performance criteria, including Accuracy, Estimated Misclassification Cost (EMC), and Area under ROC Curve (AUC) for the tested datasets. Furthermore, the best‐obtained results and the paired‐samples t‐test results show that utilizing the customized cut‐off points leads to a more accurate classification than the commonly‐used threshold value of 0.5.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".