An Incremental Parallel Particle Swarm Approach for Classification Rule Discovery from Dynamic Data
Bibliographic record
Abstract
Classification is a supervised learning technique that predicts the classes of unobserved data by employing a model built from available data. One of the efficient ways to represent this predictive model is to express it as an optimal set of classification rules to provide comprehensibility and precision, simultaneously. In this paper, we propose a novel incremental parallel Particle Swarm Optimization (PSO) approach for classification rule discovery. Our proposed method separates the training data into a set of data chunks regarding the classes and extracts optimal set of classification rules for each chunk in a parallel manner. In order to extract the rules from data chunks, we introduce an incremental PSO algorithm in which the previously extracted rules are directly employed to initialize the swarm population. Moreover, in each generation of the swarm, a tournament method is employed to substitute the weak individuals with strong extracted knowledge. To support the parallelism, we assign a PSO thread for each data chunk. As soon as all the PSO threads are completed, the extracted rules are integrated into a rule-base to construct a classification model. The evaluation results of the proposed approach on six datasets suggest that the classification precision of our proposed framework is competitive with offline learning methods and is 35% faster than its counterpart offline PSO approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".