Comparison of binary classifier and outlier detection in different equilibrium
Bibliographic record
Abstract
In the fields of financial risk control and mechanical production, the data sets with abnormal problems are always extremely unbalanced, because the most of abnormal problems occur hardly. Using this unbalanced data set to train the binary classifier, the result is often not ideal. Although there are Ensemble Learning and Grid Search methods to improve the F1 and accuracy of the classifier, in order to simplify the model, it is better to regard this financial risk control problem as an outlier detection problem than a binary classification problem. This paper uses the public data set on Kaggle, and compares the performance of the commonly used binary classification algorithms including Bayesian, Decision Tree, Random Forest, Logistic Regression Classifier, K-Nearest Neighbor (KNN), AdaBoost, One-Class SVM, Isolation Forest and Local Outlier Factor on balanced and unbalanced data sets respectively. According to the experimental results, this paper find that Bayesian is more suitable when the data set is small. Random Forest is more suitable for balanced data. For medium and large data sets with extremely unbalanced data, the effect of using One-Class SVM is better and more stable, and the effect of stable model is more important than that of unstable one.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".