Reinforcement Learning for Enhancing Classifier Performance

Classifier performance enhancement remains a key research area in machine learning, particularly given the scarcity and high cost of labeled data. This study addresses the challenge of improving classifier performance using a novel reinforcement learning (RL) approach. Specifically, our proposed method uses RL to enhance classifier performance by representing the state as a data sample, the action as the standard deviation of Gaussian noise added to the data sample, and the reward as a composite of three components: 1) validation set, 2) noisy validation set, and 3) adversarial validation set. Through six commonly used convolution neural networks (CNNs), our Rl-based approach demonstrates higher performance metrics using breast cancer, COVID-19, and eye disease datasets. In particular, compared to these CNNs, the test accuracy for these classifications improved by 10%∼20% using RL. The implementation code is available at https://github.com/AIPMLab/Graduation-2024/tree/main/oujinajie.