SVM Fuzzy Hierarchical Classification Method for Multi-class Problems
Bibliographic record
Abstract
In this paper we present a new fuzzy classification method based on support vector machine (SVM) to treat multi-class problems. Generally, SVMs classifiers are designed to solve binary classification problem. In order to handle multi-class classification problem, we present a new method to build dynamically a fuzzy hierarchical structure from the training data. Our method is based on two main concepts: fuzzy hierarchical classification and support vector machine. First, the fuzzy hierarchical classification consists in finding relationships between objects. We introduce the transitive closure measure to discover fuzzy similarity between objects. Second, SVM is applied at each node of the hierarchy to discriminate between objects. SVM is used to divide the original problem into sub-problems. We combine multiple binary SVMs to solve multi-class classification. We use equivalence classes to regroup similar objects into single class. Finally, we get a direct hierarchy of classes. Our experimental results show that the proposed model of fuzzy classification is very effective and efficient to handle multiclass problem.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".