关键词: 人工智能, 高斯案例推理, 相似度, 高斯指标, 预测模型

Abstract: To resolve the problem of the limitation of low accuracy and reliability in Case-based Reasoning(CBR), Gaussian Case-based Reasoning(GCBR)is proposed. In this research, based on the research on similarity measure on the basis of Euclidean metric, a new means of dealing with similarity measure by employing Gaussian transformations in place of the Euclidean metric is proposed. The concept of distance proportion is introduced to make a transformation that can transfer distances between a pair of cases on each feature into Gaussian indicators, and similarity between two cases will be derived. On that basis, the nearest-neighbor is applied to retrieve case similarity, and the predictive value will be generated by modifying cases. Taking the prediction for the risks of logistics outsourcing enterprise for example, compared with classical CBR model on the basis of Euclidean metric and the Grey CBR, GCBR model statistically and significantly performs better on predictive accuracy, which illustrates the feasibility of applying GCBR model to predict the risks of logistics outsourcing enterprise.

Key words: artificial intelligence, gaussian case-based reasoning, similarity, gaussian index, prediction model