Data Augmentation and Class Imbalance Compensation Using CTGAN to Improve Gas Detection Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of sensors in gas detection systems for environmental monitoring is largely affected by sensor drift over time which reduces accurate classification. This drift can be minimized by using machine learning models trained on sensor data. Here, two different machine learning models are trained on the Gas Sensor Array Drift Dataset. However, this dataset, which has been collected over three years, suffers not only from drift but also from class imbalance. As a result, machine learning models cannot perform properly on this dataset. To address these problems, this paper introduces an innovative methodology for data compensation and augmentation using Conditional Tabular Generative Adversarial Networks (CTGAN). By employing this methodology, we can counteract the class imbalance and limit drift by bringing diversity to the dataset, which in turn improves the accuracy of machine learning models for gas detection systems. With class imbalance compensation, Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) achieved an improvement in classification accuracy in five batches, up to 20% for certain batches. Through data augmentation, they reached higher accuracy across six batches, with certain batches exceeding a 10% improvement. These achievements highlight the effectiveness and reliability of the use of synthetic data generation in tabular data for sensors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it