AN INTELLIGENT DECISION-MAKING SYSTEM BASED ON MULTIPLE CLASSIFIERS UPDATED USING CONFIDENCE RATES AND STRESS PARAMETERS
Why this work is in the frame
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Bibliographic record
Abstract
We develop an intelligent multi-classifier decision-making system for multi-class classification tasks. The proposed system called I-MDS (Intelligent Multiple Decision System) uses a dynamic scheme to combine the information provided by the individual classifiers and make a classification decision. The individual classifiers in the system are interconnected and use a negotiation scheme to come up with a unified decision. During the interactive and reactive negotiation process, individual classifiers are allowed to revaluate their confidence in their individual decisions and to respond to a system-wise stress parameter that keeps increasing as long as the system does not reach a unified decision. If after a certain number of negotiation rounds the system can not reach a unified decision, the input pattern is rejected. The proposed systems were tested on multi-class classification problems from the UCI repository and were shown to produce better classification rates and fewer misclassifications than majority voting combination technique.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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