Evaluating JA-ABC5 Hyperparameter Optimisation with Classifiers

Because of its simplicity, flexibility, and robustness, the Artificial Bee Colony (ABC) algorithm, a swarm intelligence-based optimisation method, has been widely applied in a variety of fields. However, its application in hyperparameter optimisation for machine learning classifiers deserves exploration. The effectiveness of ABC and its modified version, JA-ABC5, for hyperparameter optimisation across various classifiers, including Support Vector Machine (SVM) and K-Nearest Neighbour (KNN), is studied in this research. The Wisconsin dataset is used to evaluate the performance of these classifiers, and the hyperparameters are optimised using the JA-ABC5 algorithm. The performance of JA-ABC5 is compared to that of grid search, standard ABC, Bayesian optimisation, and random search. The findings demonstrate that JA-ABC5 performs admirably in terms of SVM, with accuracy, specificity, and sensitivity of 98.59%, 99.51%, and 98.34%, respectively. Its performance in KNN is comparable. This study extends our knowledge of machine learning model optimisation, which has the potential to enhance the effectiveness of these models across a range of applications.