Selective Sampling Designs to Improve the Performance of Classification Methods
Bibliographic record
Abstract
Selective Sampling design refers to the situation where a study has a fixed number of observations but can decide to allocate them differently among the variables during the data gathering phase, such that some variables will have a greater ratio of missing values than others. In particular, we can decide to allocate more, or less missing values to uncertain variables: those for which the relative frequency is closer to 50% (higher uncertainty), or further from 50% (lower certainty). The main objective of the study is to investigate how a Selective Sampling process helps improve the performance of classification methods. This study specifically asks: "Can Selective Sampling affect the performance of the classification methods?" We focus on the three different classification models of Naïve Bayes, Logistic Regression and Tree Augmented Naive Bayes (TAN) for binary datasets. Three different schemes of sampling are defined: 1-Uniform (random samples) as a baseline, 2-Most Uncertain (higher sampling rate of uncertain items) and 3-Least Uncertain (lower sampling rate of uncertain items). We investigate the impacts of these different schemes on the performance of the three models on 11 different datasets. The results from 100 fold cross-validation show that Selective Sampling in all of the datasets improves the prediction performance of the TAN model and, in more than half of the datasets (54.6%), brings a higher prediction performance to Naïve Bayes and Logistic Regression classifiers.
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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 | 0.115 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".