HACO2 method for evolving hyperbox classifiers with ant colony optimization

A method, called HACO2 (Hyperbox classifier with Ant Colony Optimization - type 2), is proposed for evolving a hyperbox classifier using the ant colony meta-heuristic. It reshapes the hyperboxes in a nearoptimal way to better fit the data, improving the accuracy and possibly indicating its most discriminative features. HACO2 is validated using artificial 2D data showing over 90% accuracy. It is also applied to the benchmark iris data set (4 features), providing results with over 93% accuracy, and to the MIS data set (11 features), with almost 85% accuracy. For these sets, the two most discriminative features obtained from the method are used in simplified classifiers which result in accuracies of 100% for the iris and 83% for the MIS data sets. Further modifications (automatic parameter setting), extensions (initialization short comings) and applications are discussed.