A Wrapper-Based Combined Recursive Orthogonal Array and Support Vector Machine for Classification and Feature Selection
Bibliographic record
Abstract
In data mining, classification problems are among the most frequently discussed issues. Feature selection is a very important pre-processing function in the vast majority of classification cases. Its aim is to delete irrelevant or redundant features in order to reduce the feature dimension and computing complexity and increase the accuracy of classification. Current feature selection methods can be roughly divided into the filter method and the wrapper method. The former chooses the feature subset before classifying, whereas the latter chooses the feature subset during the classification procedure. In general, wrapper methods result in better performance than filter methods, but they are time-consuming. This paper therefore proposes a wrapper method called OA-SVM that uses an orthogonal array (OA) to make systemic rules of feature selection and uses support vector machine (SVM) as the classifier. The proposed OA-SVM is employed to test eight UCI databases for the classification problem. The results of these experiments verify that the proposed OA-SVM for feature selection can effectively delete irrelevant or redundant features, thereby increasing classification accuracy.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".