Adaptation of exclusion / inclusion hyperboxes for classification of complex data

Exclusion/inclusion hyperbox classification has demonstrated significant advantages in terms of its ability to cover topologically complex data structures with a relatively few hyperboxes thus resulting in the superior interpretability of classification results. However, the size of exclusion hyperboxes may occasionally become prohibitive if the data classes are grouped in a particularly unfavorable way in the pattern space. In this study we consider adaptation of the maximum size of hyperboxes in response to the ratio of the exclusion to inclusion hyperboxes. Two alternative adaptation strategies are being considered: (i) the adaptation of the size of all hyperboxes and (ii) the adaptation of the size of hyperboxes that fall within the previously identified exclusion area. The tradeoff between the number and the complexity of the classification rules implied by the two strategies is assessed on a set of sample classification problems.