RisQNet: Rescuing SMEs from Financial Shocks with a Novel Networked-Loan Risk Assessment

Abstract

More accurate construction of brain effective conncetivity networks remains a great challenge to achieve accurate auxiliary diagnosis of brain diseases and in-depth exploration of brain function. However, existing methods only consider higher-order or non-stationary assumptions, rather than simultaneously constructing higher-order and non-stationary networks. Among many existing methods, Bayesian network methods demonstrate superior network structure learning ability. In this work, the forward-backward search (FBS) method is optimized by using brain active information, which is improved to a higher-order network structure learning method, called TSTAI. Firstly, in the process of non-stationary network structure learning, two-stage idea is used to search the change points. Then, in the process of learning higher-order network structure, FBS method is combined with two kinds of brain active information to improve the condition set filtering process and scoring function, respectively. Finally, the pruning strategy is used to reduce the search space. Extensive experiments on simulated and real data demonstrate the effectiveness of TSTAI. Through experiments, the TSTAI is compared with state-of-the-art higher-order network construction methods, and the proposed method achieves an improvement of 3.6% and 17.4% respectively in the network construction accuracy.