Improving imbalanced dataset classification using Conditional Classifier-Generator (cCGen)
Bibliographic record
Abstract
Thermal Comfort Data is critical to generate machine learning models for efficient heating and cooling systems. However, thermal comfort datasets are often highly imbalanced due to subjective user feedback, thus making it challenging to accurately predict both majority and minority classes. This demands the use of data synthesis techniques prior to training classification models to balance the datasets. Commonly used techniques like Synthetic Minority Over-sampling Technique (SMOTE) or Adaptive Synthetic Sampling Method (ADASYN) often compromise testing accuracy and more sophisticated techniques like Conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) are significantly expensive to train. In this paper we propose a novel data augmentation algorithm called Conditional Classifier-Generator (cCGen) to address these two issues. We evaluated the performance of cCGen with real thermal comfort data against SMOTE, ADASYN and cWGAN-GP at different imbalance ratios. Our experiments reveal that our approach can produce better F1 scores than other sampling methods while being more than 10 times faster than cWGAN-GP and not compromising test accuracy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".