Constructing a Fuzzy Rule Based Classification System Using Pattern Discovery
Why this work is in the frame
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Bibliographic record
Abstract
Pattern discovery (PD), an algorithm which discovers patterns based on a statistical analysis of training data was used to generate rules for a fuzzy rule based classification system (FRBCS). Classification performance of the FRBCS when using rules discovered by the PD algorithm and of the PD algorithm functioning as a classifier applied to a number of linearly and non-linearly separable continuous-valued data sets was compared. The results indicate an increased performance for the FRBCS. The improvement comes through both an increase in correct classifications and a decrease in the error rate in the class distributions studied. The use of trapezoidal shaped input membership functions applied to the input data values allowed vagueness in the input events to be modelled and resulted in a more robust determination of the characteristics of the input data which in turn resulted in more accurate classification. In addition, the standard use of a co-occurrence based weighting of the rules by the FRBCS outperformed the weight-of-evidence based selection and use of input patterns by the PD classifier.
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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 | 0.000 | 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