Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 399–402 (2017)
Abstract. Finding efficient solutions for search and optimisation problems has inspired many researchers to utilise nature informed algorithms, where the interactions in swarm could lead to promising solutions for challenging problems. One problem in machine learning is class imbalance, which occurs in real-world applications such as medical diagnosis. This problem can bias the classification or make it entirely out of context where the algorithms being applied to classify the data can potentially ignore the important minority class instances. In this paper, a parameters optimisation algorithm is proposed, which uses a swarm intelligence technique, dispersive flies optimisation, to optimise the support vector machine kernel's parameters and perform cost sensitive learning to improve the classifier's performance on imbalanced data.