论文标题
信用评估的在线整洁 - 有顺序数据的动态问题
Online NEAT for Credit Evaluation -- a Dynamic Problem with Sequential Data
论文作者
论文摘要
在本文中，我们描述了神经进化的应用到P2P贷款问题，其中根据流数据更新了信用评估模型。我们应用了增强拓扑（NEAT）的算法神经进化，该算法尚未普遍应用于信用评估领域。除了将方法与其他广泛应用的机器学习技术进行比较之外，我们还开发和评估了对算法的几种增强功能，这些增强功能使其适用于与问题相关的在线学习的特定方面。其中包括处理不平衡的流数据，高计算成本以及随着时间的推移维持模型相似性，那就是训练使用新数据的随机学习算法，但最小化模型更改，除非为模型性能带来明显的好处
In this paper, we describe application of Neuroevolution to a P2P lending problem in which a credit evaluation model is updated based on streaming data. We apply the algorithm Neuroevolution of Augmenting Topologies (NEAT) which has not been widely applied generally in the credit evaluation domain. In addition to comparing the methodology with other widely applied machine learning techniques, we develop and evaluate several enhancements to the algorithm which make it suitable for the particular aspects of online learning that are relevant in the problem. These include handling unbalanced streaming data, high computation costs, and maintaining model similarity over time, that is training the stochastic learning algorithm with new data but minimizing model change except where there is a clear benefit for model performance
