期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/179060
出版社：Hindawi Publishing Corporation
摘要：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.
