Pruning boxes in a box-based classification method

In this work we address an extension of box clustering in supervised classification problems that makes use of optimization problems to refine the results obtained by agglomerative techniques. The central concept of box clustering is that of homogeneous boxes that give rise to overtrained classifiers under some conditions. Thus, we focus our attentions on the issue of pruning out redundant boxes, using the information gleaned from the other boxes generated under the hypothesis that such a choice would identify simpler models with good predictive power. We propose a pruning method based on an integer optimization problem and a family of sub problems derived from the main one. The overall performances are then compared to the accuracy levels of competing methods on a wide range of real data sets. The method has proven to be robust, making it possible to derive a more compact system of boxes in the instance space with good performance on training and test data.