Handling Multi-Class Problem by Intuitionistic Fuzzy Twin Support Vector Machines Based on Relative Density Information
Bibliographic record
Abstract
The intuitionistic fuzzy twin support vector machine (IFTSVM) merges the idea of the intuitionistic fuzzy set (IFS) with the twin support vector machine (TSVM), which can reduce the negative impact of noise and outliers. However, this technique is not suitable for multi-class and high-dimensional feature space problems. Furthermore, the computational complexity of IFTSVM is high because it uses the membership and non-membership functions to build a score function. We propose a new version of IFTSVM by using relative density information. This idea approximates the probability density distribution in multi-dimensional continuous space by computing the K-nearest-neighbor distance of each training sample. Then, we evaluate all the training points by a one-versus-one-versus-rest strategy to construct the k-class classification hyperplanes. A coordinate descent system is utilized to reduce the computational complexity of the training. The bootstrap technique with a 95 % confidence interval and Friedman test are conducted to quantify the significance of the performance improvements observed in numerical evaluations. Experiments on 24 benchmark datasets demonstrate the proposed method produces promising results as compared with other support vector machine models reported in the literature.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".