A New Decision Tree Based on Intuitionistic Fuzzy Twin Support Vector Machines
Bibliographic record
Abstract
Effectively classifying anomalies in a multi-class setting holds significant importance in domains such as medical datasets, fraud detection, and anomaly detection. This task presents challenges that include efficient training on large datasets, accurate classification in imbalanced scenarios, and sensitivity to high imbalance ratios (IR). This paper introduces a novel approach, the Intuitionistic Fuzzy Twin Support Vector Machine-based Decision Tree (NDT-IFTSVM), aimed at addressing these issues. NDT-IFTSVM integrates IFTSVM and decision tree methodologies, offering an efficient solution for multi-class classification. The proposed algorithm constructs a decision tree comprised of a series of two-class IFTSVMs. To enhance balance and separability, the multi-class method iteratively divides into two sets based on distance between class centres and instance distribution. This recursive process continues until each subset exclusively contains a single class, facilitating effective classification. To handle highly imbalanced datasets, NDT-IFTSVM incorporates a rational weighting strategy. Additionally, we refine NDT-IFTSVM by introducing a regularization term that maximizes the margin between the bounding and proximal hyperplanes, mitigating the impact of noise and outliers. Finally, a coordinate descent system with shrinking by an active set is applied to reduce the computational complexity. Numerical evaluations employ the bootstrap technique with a 95% confidence interval and statistical tests to quantify the significance of performance improvements. Experimental results on 12 datasets demonstrate the efficacy of the proposed method, showcasing promising outcomes compared to other techniques documented 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".