Density Fixing: Simple yet Effective Regularization Method based on the Class Prior
This is an incremental improvement for machine learning practitioners dealing with overfitting in supervised and semi-supervised learning.
The paper tackles overfitting due to limited labeled data by proposing density-fixing, a regularization method that forces models to approximate class prior distributions, improving generalization performance with experimental validation on benchmark datasets.
Machine learning models suffer from overfitting, which is caused by a lack of labeled data. To tackle this problem, we proposed a framework of regularization methods, called density-fixing, that can be used commonly for supervised and semi-supervised learning. Our proposed regularization method improves the generalization performance by forcing the model to approximate the class's prior distribution or the frequency of occurrence. This regularization term is naturally derived from the formula of maximum likelihood estimation and is theoretically justified. We further provide the several theoretical analyses of the proposed method including asymptotic behavior. Our experimental results on multiple benchmark datasets are sufficient to support our argument, and we suggest that this simple and effective regularization method is useful in real-world machine learning problems.