School of Science, Beijing Jiaotong University, Beijing 100044, China.
Guanghua School of Management, Peking University, Beijing, China.
Comput Intell Neurosci. 2022 May 9;2022:7650207. doi: 10.1155/2022/7650207. eCollection 2022.
In recent years, there has been increasing interest in exploring diversified features to measure small and medium-sized enterprises (SMEs) credit risk. Path-based features, revealing logical connections between SMEs, are widely adopted as informative feature kinds for causal inference in credit risk evaluation. Since there may exist thousands of feature paths to the target enterprise, to evaluate its credit risk, how to select the most informative path-based features becomes a challenging problem. To solve the problem, in this paper, we propose a novel method of feature selection, considering both similarity and importance on features' structured semantics as the factors of informativeness. With this, the proposed method can effectively rank both conventional and path-based features together. Furthermore, to improve the efficiency of the method, a heuristic algorithm is proposed to fast search for the candidate features. Through extensive experiments, we show our method performs competitively with other state-of-the-art selection methods.
近年来，人们越来越关注探索多样化的特征来衡量中小企业 (SMEs) 的信用风险。基于路径的特征揭示了中小企业之间的逻辑联系，被广泛用作信用风险评估中因果推理的信息特征类型。由于可能存在数千条通往目标企业的特征路径，因此要评估其信用风险，如何选择最具信息量的基于路径的特征成为一个具有挑战性的问题。为了解决这个问题，在本文中，我们提出了一种新的特征选择方法，同时考虑特征结构语义的相似性和重要性作为信息量的因素。通过这种方式，所提出的方法可以有效地对传统特征和基于路径的特征进行综合排序。此外，为了提高方法的效率，我们提出了一种启发式算法来快速搜索候选特征。通过广泛的实验，我们表明我们的方法与其他最先进的选择方法具有竞争力。
