A feasible method for training classified data with sparsity

Abstract

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.

Study on Interlaminar Fracture Toughness of the CNTs-CF/epoxy Resin Composites

“…By using carbon nanotubes with surface grafted functional groups, the dispersion of carbon nanotubes in epoxy resin and the bonding strength between carbon nanotubes and resin matrix can be improved [7,8]. The action mechanism mainly includes the following two points: (1) the functional groups on the surface of carbon nanotubes can react with epoxy groups during the curing process, resulting in effective covalent bonding (chemical bond) between carbon nanotubes and matrix, which greatly improves the interfacial bonding strength between them [9,10]; (2) As a dendritic structure, the functional groups on the surface of carbon nanotubes can increase the friction force required when carbon nanotubes are pulled out of the matrix, so as to improve the fracture initiation of epoxy resin(or epoxy resin based composites) [11,12].…”