Online NEAT for Credit Evaluation -- a Dynamic Problem with Sequential Data
This work addresses dynamic credit evaluation for P2P lending platforms, but it is incremental as it adapts an existing method to a new domain with specific modifications.
The paper tackled credit evaluation in P2P lending by applying Neuroevolution of Augmenting Topologies (NEAT) to update models with streaming data, developing enhancements for online learning challenges like unbalanced data and high computation costs, and comparing it to other machine learning techniques.
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