A new multi-objective evolutionary algorithm based on convex hull for binary classifier optimization

In this paper, we propose a novel population-based multi-objective evolutionary algorithm (MOEA) for binary classifier optimization. The two objectives considered in the proposed MOEA are the false positive rate (FPR) and the true positive rate (TPR), which are the two measures used in the ROC analysis to compare different classifiers. The main feature of our MOEA is that the population evolves based on the properties of the convex hulls defined in the FPR-TPR space. We discuss the application of our MOEA to determine a set of fuzzy rule-based classifiers with different trade-offs between FPR and TPR in lung nodule detection from CT scans. We show how the Pareto front approximation generated by our MOEA is better than the one generated by NSGA-II, one of the most known and used population-based MOEAs.