SIGMOID SUPPLEMENTED DECISION STRUCTURES FOR EVIDENCE SENSITIVITY LEARNING
Bibliographic record
Abstract
During decision making for classification it is desirable to predict an outcome when not all of the evidence is available.Consider medical diagnosis.If a doctor is trying to determine the cause of a patient's ailment, often they are presented with a subset of potential evidences for or against a particular diagnosis.As the doctor runs more tests and the patients symptoms evolve, the doctor becomes more confident in their evaluation.It is critical that the decision maker be as confident in their decision as possible with as few evidences as are available.The goal of this paper is to improve the ability to predict the final decision given only a subset of the total information.By exploiting interdependencies and probabilistic relationships between the evidences, the confidence of prediction of a decision making tool can be improved through machine learning.Given some complete set of evidences, the Analytical Hierarchy Process (AHP) provides a method of weighting the nodes in a decision structure to synthesize a decision that reflects the opinion of a subject matter expert (SME).By truncating the comparison matrices produced for the AHP, weights can be generated for decision structures that are lacking inputs, known as deficient decision structures.This paper proposes a method of sigmoid node supplementation to the standard decision structure.Using machine learning the parameters of these sigmoid nodes can be optimized so that the output of deficient decision structures can be vastly improved for prediction of the output of the complete decision structure.This method preserves the original weights derived through the AHP and thus the relative importance of evidences is maintained after learning is undergone.An example will illustrate the improved confidence in prediction that can be achieved by adjusting the sensitivity of the supplemented sigmoid nodes.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".