A misclassification cost-minimizing evolutionary-neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)-based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real-world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back-propagation artificial neural network, integrated cost preference-based linear mathematical programming-based minimize squared deviations, linear integrated cost preference-based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost-minimizing approaches.