Improving imbalanced dataset classification using Conditional Classifier-Generator (cCGen)

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.