计算机工程与应用2019，Vol.55Issue(13)：36-41,6.DOI:10.3778/j.issn.1002-8331.1810-0126
自注意力信用评估模型
Self-Attention Credit Scoring Model
摘要
Abstract
In the credit scoring problem, the user information contains both category data and numerical data. Traditional artificial intelligence-based credit scoring algorithms usually transform the category data into one-hot vectors and joints them with numerical data, as the input of the discriminator. In contrast, this paper extracts vectors of category data based on the word embedding techniques which are popularly used in the natural language processing problem. After that, the set of the word vectors is analogized to a"sentence", and the input feature is extracted from the"sentence"based on the self-attention mechanism. Finally, a Multi-Layer Perception(MLP)neural network is used to predict the probability of default. The new model is trained end-to-end by the back propagation method. Experimental results show the proposed new model achieves better performance than six other baselines on three well-known benchmark datasets.关键词
信用评估/自注意力机制/词嵌入/特征提取/深度神经网络Key words
credit scoring/ self-attention mechanism/ word embedding/ feature extraction/ deep neural network分类
信息技术与安全科学引用本文复制引用
LIU Xinyang,QU Yanwen,ZHOU Qiyun..自注意力信用评估模型[J].计算机工程与应用,2019,55(13):36-41,6.基金项目
国家自然科学基金(No.61562041,No.61866018). （No.61562041,No.61866018）
