A random forest classifier with cost-sensitive learning to extract urban landmarks from an imbalanced dataset
Bibliographic record
Abstract
Urban landmarks play an important role as spatial references in spatial cognition, navigation, map design and urban planning. However, the current landmark extraction methods do not consider the imbalance between the landmark and non-landmarknon-landmark samples in a dataset, so the extraction results are biased toward the class with the majority of sample data, resulting in poor classification performance for the class with the fewest sample data. This study introduces a random forest (RF) classifier combined with cost-sensitive learning to extract urban landmarks automatically from a basic spatial database. First, the optimal feature set is determined according to the importance of features. Next, a cost-sensitive RF algorithm is applied to extract landmarks, which determines the misclassification cost according to the class distribution, and each decision tree is weighted by the classification results. The method has good performance, with a recall and area under the ROC curve (AUC) greater than 90%, and the model is also applicable to small sample sets, which can reduce the cost of manual labor.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".