Mining relational data through correlation-based multiple view validation
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
Commercial relational databases currently store vast amounts of real-world data. The data within these relational repositories are represented by multiple relations, which are inter-connected by means of foreign key joins. The mining of such interrelated data poses a major challenge to the data mining community. Unfortunately, traditional data mining algorithms usually only explore one relation, the so-called target relation, thus excluding crucial knowledge embedded in the related so-called background relations. In this paper, we propose a novel approach for classifying relational such domains. This strategy employs multiple views to capture crucial information not only from the target relation, but also from related relations. This information is integrated into the relational mining process. The framework presented here, firstly, explore the relational domain to partition its features space into multiple subsets. Subsequently, these subsets are used to construct multiple uncorrelated views, based on a novel correlation-based view validation method, against the target concept. Finally, the knowledge possessed by multiple views are incorporated into a meta-learning mechanism to augment one another. Based on this framework, a wide range of conventional data mining methods can be applied to mine relational databases. Our experiments on benchmark real-world data sets show that the proposed method achieves promising results both in terms of overall accuracy obtained and run time, when compared with two other relational data mining approaches.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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