A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Abstract

This study suggests a methodology called a smart ubiquitous data mining (UDM) that consolidates homogeneous models in a smart ubiquitous computing environment. It tests the suggested model with financial datasets. It basically induces rules from the dataset using diverse rule extraction algorithms and combines the rules to build a metamodel. This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. To verify the feasibility and effectiveness of UDM, personal credit data and personal loan data provided by a Financial Holding Company (FHC) were used in this study. Empirical results indicated that UDM outperforms other models such as LR, BFM, MLP, C5.0, and NR.