Tuning structural parameters of neural networks using genetic algorithm: A credit scoring application
Bibliographic record
Abstract
Abstract Neural networks (NNs) have successfully been applied to classification problems including credit scoring. The tuning of the structural parameters of the NNs has a direct impact on their accuracy. In this paper, a hybrid approach based on the genetic algorithm (GA) is proposed to adjust the structural parameters of a classifier NN to achieve high accuracy. Two well‐known credit scoring datasets—Australian and German datasets—are used to test the proposed approach. The results indicate that the proposed hybrid approach is able to successfully tune the structural parameters, while the accuracy of classification is enhanced and its complexity dramatically diminished in comparison with other existing approaches. The performance of the proposed algorithm has been investigated through statistical analysis The best‐known solutions achieved by the proposed approach have an accuracy equal to 97.78% and 87.1% for Australian and German datasets, respectively. The results indicate 2.68% and 0.1% improvement in comparison with the best results reported in the literature, respectively. This improvement is important for real cases in which millions of loans are allocated using credit scoring approaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".