Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution

In this research, intelligent classifiers for disease diagnosis are designed that use classifier parameters, such as cost, tolerance, gamma and epsilon, with multi-objective evolutionary algorithms. The multiple objective functions are prediction accuracy, sensitivity and specificity. This paper employs a Sequential Minimal Optimization (SMO), a variant of the classical Support Vector Machine (SVM), as the base classifier in conjunction with three popular evolutionary algorithms (EA), namely, Elephant Herding Optimization (EHO), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), for parameter evolution. A new cuboids based initial population generation mechanism was also introduced to hybridize EHO, called CEHO. The performance of CEHO is compared with the other three EAs (EHO, MOEA/D and NSGA-II) over 17 medical engineering datasets, and pertinent statistical tests were conducted to substantiate their performances. The results demonstrate that the proposed CEHO exhibit better to competitive results across all datasets.