Active AODE learning based on a novel sampling strategy and its application
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we first present active Averaged One-Dependence Estimator (AODE) learning classification model, which can improve the performance of AODE by selecting and asking experts to label the samples only with maximum information. Several common sampling strategies for active learning are discussed. Unfortunately, these methods can get the outlier, which will lead to scale up the classification-reduced error and high complexity. Motivated by those analyses, we propose a new active learning strategy, which is based on the uncertainty sampling and classification accuracy loss sampling strategy. Experimental results on three UCI standard data sets and a real remote sensing data set show that the AODE classification model and our novel active learning strategy can get better classification accuracy with fewer labelled samples than that of the state-of-the-art approaches for active learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it