Multiobjective hybrid monarch butterfly optimization for imbalanced disease classification problem

Datasets obtained from the real world are far from balanced, particularly for disease datasets, since such datasets are usually highly skewed having a few minority classes apart from one or more prominent majority classes. In this research, we put forward the novel hybrid architecture to handle imbalanced binary disease datasets that arrives upon the efficient combination of Support vector machine (SVM) classifier's sensitive parameter values for improved performance of SVM by means of an Evolutionary algorithm (EA), namely monarch butterfly optimization (MBO). In this paper, MBO is used to enumerate three objectives, namely prediction accuracy (PAC), sensitivity (SEN), specificity (SPE). Additionally, we propose a Totally uni-modular matrix (TUM) and limit points based non-dominated solutions selection for deciding local and global search and to generate an efficient initial population respectively. Since these two greatly affect the performance of EAs, the performance of the proposed hybrid architecture is tested on 18 disease datasets having binary class labels and the results obtained demonstrate improvements using the proposed method. For the majority of the datasets, either 100% sensitivity and/or specificity were attained. Moreover, pertinent statistical tests were carried out to ascertain the performances obtained.