An Efficient Approach to Learn an Effective Hierarchy of a Set of OOBN Classes
Bibliographic record
Abstract
Day by day, Bayesian networks are getting popular for solving real-life problems. However, it is difficult to build Bayesian decision networks (BNs) to solve large scale real world problems. Using object-oriented Bayesian networks (OOBNs) is one strategy to deal with the scalability issue. OOBNs make it possible by providing researchers with the facility to design classes and build models with a modular and hierarchical architecture, which increases reuse and maintenance facilities. Sharing properties down the hierarchy of classes, known as “inheritance” in OO-paradigm, is a key idea to increase the reusability and tackle scalability issue. It means that one can share or reuse components and behaviors of an entity known as object or class. Previously, a framework of OOBN was proposed to contain inheritance and all other aspects of OO-paradigm. Recently, in 2022, an extension was proposed to learn hierarchy of OOBN classes. However, such an extension is still suboptimal. In this paper, we identify some scopes to improve the learning technique. We propose and implement a new algorithm and then analyze it empirically and asymptotically. We use both synthetic and real-world data in the empirical analysis. The analysis shows that our proposed algorithm is more effective and efficient, especially in terms of reusability.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".