Diagnostic with incomplete nominal/discrete data
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
Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise applicationof readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown.Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, nospecial handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation.Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour,and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the enteredmissing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification.The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents anumber of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing downof the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. Theproposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating asignificant improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it