Behavioral learning of vessel types with fuzzy-rough decision trees
Bibliographic record
Abstract
A reliable and efficient characterization of vessel activities along coastal regions is of crucial importance for maritime domain awareness. With increased navigational flows across all waterways and the worldwide dissemination of active and passive vessel tracking modalities, learning a vessel’s behavior is becoming a strategic priority for maritime operators and decision makers. In this paper, we propose an interpretable computational model based on fuzzy-rough decision trees (FRDTs) to predict the vessel type given a summary vector in the form of descriptive track features that include kinematic, static and environmental information. The track summaries are generated from the fusion of Automatic Identification System (AIS), Synthetic Aperture Radar (SAR) and Canada weather reports. Our methodology uses fuzzy rough sets to discard irrelevant features on the basis of their dependency of the vessel type, prior to the iterative construction of the FRDT. Empirical results with a real-world data set in the east coast of North America confirm that the proposed approach is able to accurately assign the correct label (i.e., type) to previously unseen vessels in over 80% of the cases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".