Decision tree approaches for zero-inflated count data

There have been many methodologies developed about zero-inflated data in the field of statistics. However, there is little literature in the data mining fields, even though zero-inflated data could be easily found in real application fields. In fact, there is no decision tree method that is suitable for zero-inflated responses. To analyze continuous target variable with decision trees as one of data mining techniques, we use F-statistics (CHAID) or variance reduction (CART) criteria to find the best split. But these methods are only appropriate to a continuous target variable. If the target variable is rare events or zero-inflated count data, the above criteria could not give a good result because of its attributes. In this paper, we will propose a decision tree for zero-inflated count data, using a maximum of zero-inflated Poisson likelihood as the split criterion. In addition, using well-known data sets we will compare the performance of the split criteria. In the case when the analyst is interested in lower value groups (e.g. no defect areas, customers who do not claim), the suggested ZIP tree would be more efficient