OptBA: Optimizing Hyperparameters with the Bees Algorithm for Improved Medical Text Classification
This addresses the problem of hyperparameter optimization for researchers and practitioners in healthcare, but it is incremental as it applies an existing algorithm to a specific domain.
The paper tackles hyperparameter optimization for deep learning models in medical text classification by proposing OptBA, which uses the Bees Algorithm to fine-tune hyperparameters, resulting in an accuracy improvement of approximately 1.4%.
One of the main challenges in the field of deep learning is obtaining the optimal model hyperparameters. The search for optimal hyperparameters usually hinders the progress of solutions to real-world problems such as healthcare. Previous solutions have been proposed, but they can still get stuck in local optima. To overcome this hurdle, we propose OptBA to automatically fine-tune the hyperparameters of deep learning models by leveraging the Bees Algorithm, which is a recent promising swarm intelligence algorithm. In this paper, the optimization problem of OptBA is to maximize the accuracy in classifying ailments using medical text, where initial hyperparameters are iteratively adjusted by specific criteria. Experimental results demonstrate a noteworthy enhancement in accuracy with approximately 1.4%. This outcome highlights the effectiveness of the proposed mechanism in addressing the critical issue of hyperparameter optimization and its potential impact on advancing solutions for healthcare. The code is available publicly at \url{https://github.com/Mai-CS/OptBA}.