Adaptive focal loss with personality stratification for stably mitigating hard class imbalance in multi-dimensional personality recognition.

Enabling personalized interactions and gaining deeper insights into user behavior has become increasingly important for improving human-computer interaction. However, the natural class imbalance in personality datasets, reflecting the real-world distribution of traits where certain personality types are less represented, poses a major challenge for classification models and often results in biased performance. This research explores a range of class imbalance mitigation techniques (CIMTs), including sampling methods and loss functions, to address this issue. We introduce Adaptive Focal Loss with Personality-Stratified Dataset Splitting, a novel approach specifically designed to mitigate class imbalance while stabilizing performance in multi-dimensional personality recognition. Additionally, we analyze multiple evaluation techniques., including regular accuracy, F1 score, and balanced accuracy, recommending the latter for a more comprehensive and fair performance analysis. Our experiments reveal that the proposed stratified techniques with label representation are vital for making the performance not sensitive to dataset splitting, while Adaptive Focal Loss significantly enhances classification performance on imbalanced datasets by incorporating trainable hyperparameter, also effectively addressing the challenges of hyperparameter sensitivity and selection. On average across dimensions, our method improves balance accuracy by up to 7% on the Kaggle dataset and 5% on the Essays dataset compared to regular training, while maintaining minimal computational overhead. These findings mark a critical step toward more robust and equitable personality recognition systems using relatively computationally efficient models. The related research resources, including the code repository and datasets, are available and can be accessed at: https://research.jingjietan.com/?q=AFLPS