A Rough Set System for Mining from Streaming Data
Bibliographic record
Abstract
In the era of big data, dynamic data have become more popular than static data because high volumes of data can be generated and collected at a rapid rate. Although rough set theory has been widely used as a framework to mine decision rules from information system, most of the existing algorithms were not designed to handle streaming data. Hence, in this paper, we present a system based on rough set theory to mine decision rules from streaming data. In particular, our rough set system processes data streams on two bases (namely, batch-based, and aggregated-based) with three models (namely, landmark, sliding window, and time-fading models) for a total of six combinations of stream processing and mining models (e.g., batch-based landmark model). Evaluation results on comparisons with existing works on several benchmark datasets show the benefits—in terms of both accuracy improvements and runtime reduction—and the practicality of our rough set system in mining data streams.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".