Cutting down high dimensional data with Fuzzy weighted forests (FWF)
Bibliographic record
Abstract
Takagi-Sugeno-Kang (TSK) rule-based fuzzy systems struggle to deal with high dimensional data and suffer from the curse of dimensionality. As the number of input features increases, the number of rules increases exponentially, which reduces the model's interpretability rapidly. This paper presents a novel fuzzy weighted forest aggregation method to effectively model high dimensional data by reducing the number of fuzzy rules, without sacrificing accuracy. The fuzzy weighted forest is comprised of several fuzzy weighted trees. Each tree is created based on a subset of features captured across different parts of the input space. Given n input features and N samples, every fuzzy tree is randomly assigned n′ features and N′ samples, where n′ and N′ are significantly smaller than n and N respectively. Each path within a tree, from root to leaf, forms a fuzzy rule. The non-leaf nodes represent the antecedents of rules, and the leaf node represents the consequents. This study shows how the proposed method utilizes pruning to significantly reduce the number of fuzzy rules. This method therefore creates a less complex model whilst achieving high accuracy comparable with, and sometimes better than, existing state-of-the-art TSK-based fuzzy models.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".