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Family harmony is an important part of social stability and harmony. Facing the rapid growth of divorce disputes, how to hear the cases quickly and fairly is an urgent matter to be solved. Based on judgment documents from courts located in Southwest of China, this paper studies how to evaluate and predict whether or not a case should be divorced. These documents are characterized by their unbalance and sparsity; besides that, most of variables are bearing with missing value. We propose a feasible method that shows high accuracy on both training and testing datasets. Concisely, oversampling and “clustering” are exploited to data pre-preparation, and recursive feature elimination is applied to deal with variables selection. In this paper we combined Random Forest and XGboost to derive a more feasible and precise model that achieves ninety percent of accuracy.

A feasible method for training classified data with sparsity