Rough set data representation using binary decision diagrams.
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Bibliographic record
Abstract
A new information system representation, which inherently represents indiscernibility is presented.The basic structure of this representation is a Binary Decision Diagram.We offer testing results for converting large data sets into a Binary Decision Diagram Information System representation, and show how indiscernibility can be efficiently determined.Furthermore, a Binary Decision Diagram is used in place of a relative discernibility matrix to allow for more efficient determination of the discernibility function than previous methods.The current focus is to build an implementation that aids in understanding how binary decision diagrams can improve Rough Set Data Analysis methods. Representaci n de datos de conjuntos aproximados mediante diagramas de decisi n binariosResumen.Se expone una nueva representacin de sistema de informacin, que incorpora inherentemente la indiscernibilidad.La estructura bsica de esta representacin es un diagrama de decisin binario.Se ofrecen los resultados de unas pruebas llevadas a cabo para convertir grandes conjuntos de datos en una representacin de sistema de informacin de diagrama de decisin binario, y se muestra cmo se puede determinar, de forma eficaz, la indiscernibilidad.Adems, se utiliza un diagrama de decisin binario en lugar de una matriz de discernibilidad relativa para permitir que la determinacin de la funcin de discernibilidad sea ms eficaz que en los mtodos anteriores.Actualmente, el inters se centra en la construccin de una implementacin que ayude a entender cmo los diagramas de decisin binarios pueden mejorar los mtodos de anlisis de datos de los conjuntos aproximados.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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