Image classification using evolving fuzzy inference systems
Bibliographic record
Abstract
Evolving fuzzy systems change by online updating of their parameters and structure; the number of fuzzy rules changes as long as there is new data. In literature, an evolving fuzzy system is mainly considered to be an unsupervised approach that builds and updates its clusters online as long as new data is available. In our previous works, we introduced a new supervised evolving fuzzy approach for segmenting medical images. In this paper, we demonstrate that this supervised evolving fuzzy approach can classify images. As an example we attempt to classify medical images based on their modalities. A set of features extracted from the image is used to train the fuzzy system with the modality class of the image as the fuzzy output. The proposed algorithm is applied to both ultrasound scans and magnetic reasoning images (MRI). The proposed algorithm is compared with the support vector machines (SVMs) and the K-nearest neighbour algorithm (KNN). The results show that evolving fuzzy systems can compete with well-establish clustering algorithms (and even surpass them) by delivering high classification rates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".