Exploring classification heterogeneity with IPA

The approach to construct predictive models of heterogeneous data is based on decomposition of a classification problem into subproblems. Applicability of this approach depends on the success in discovering the homogeneous regions in data and their coverage by the local predictive models. The importance Profile Angle (iPA) may provide an additional indication of heterogeneity considering profiles of feature importance in subproblems. in this paper iPA is evaluated on several variations of heterogeneity. The experimental study on the data sets with known data characteristics related to heterogeneity has shown that iP A is applicable when the feature merit measures are identified adequately. indication of heterogeneity provided by iP A has been verified via the gains in classification accuracy obtained in subproblems after decomposition.