Enabling Human Activity Recognition in Smart Public Transportation Systems in Presence of Dataset Imbalance
Bibliographic record
Abstract
A passenger detection method is required for smart public transportation systems. Such a method would enable control and orchestration of transit routes and schedules. To validate the method we proposed to differentiate people who appear to get into a transportation vehicle from others, we used an imbalanced WiFi-based dataset. However, the imbalanced dataset which includes majority and minority classes in the training set affects the performance of machine learning algorithms. Unreliable samples in the majority class can perturb the minority class. This paper proposes a method to efficiently classify imbalanced datasets. Then we explore the impact on classification performance of the imbalance observed in the WiAR dataset in relation to feature selection in a proposed machine-learning-based classification algorithm. The results show that the proposed method improves the F1-score performance for the minority class from 46% to 95%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".