Geodesic Fuzzy Rough Sets for Discriminant Feature Extraction
Bibliographic record
Abstract
Feature extraction is a fundamental and challenging task in machine learning, which aims at extracting a subset of significant and discriminant features from raw data for various downstream tasks. The extraction process involves mapping the original data into a space with lower dimensions while preserving the desirable information. However, the data often has hidden manifold structures, which contain neighbor sample information within the same class. Most existing methods that extract data features without considering the potentially significant manifold structures would result in poorly discriminative features. To address these challenges, we propose a novel geodesic fuzzy rough set model (GFRS) to capture those complex manifold structures embed in the data. Given GFRS, we further design a discriminant feature extraction algorithm based on graph embedding to enhance the discriminative performance of the extracted features. Extensive experiments on fourteen real-world datasets and visualization results on modified national institute of standards and technology (MNIST) digits demonstrate the effectiveness of the proposed algorithm and its superiority over baselines methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".