Distance weighted multiple classifiers systems applied to remote sensing images classification/data fusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract For a multiple classifiers system, a weighting policy is applied to fuse knowledge acquired by classifiers to arrive at an overall decision that is supposedly superior to that attainable by any one of them acting alone. The distance measured between the classifier output and its desired output can be used as a performance indicator. By adopting this performance indicator, the rms and average distance weighted multiple classifiers systems are proposed in this paper. The classification performances of utilizing various multiple classifiers systems to the application of remote sensing images classification/ data fusion are demonstrated and compared. Experimental results show that the classification accuracy is considerably improved by making use of the multiple classifiers system. In addition, the multiple classifiers systems of using distance weighted algorithms are superior to those of using Bagging and Boosting algorithms. Moreover, average distance weighted multiple classifiers system outperform rms distance weighted multiple classifiers system slightly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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