A hybrid GA-based RS-RES model for web-multimedia data management to identify determinants of credit rating status. (26th November 2019) - Chen, You-Shyang
To address practical problems for real-life applications for benefiting large-scale data sets and resources has absolute requirement in managing web-scale data. The development of an indicator for the operating sufficiency and financial status of Asian banks will be really necessary to better understand the stability of financial markets from managing distributed large-scale data. This study proposes an effective hybrid model with two stages, including: 1) it organises random forests and key reducts; and the core concept of rough-set rule exploration system (RS-RES) with a genetic algorithm setting for the benefit of feature-selection techniques to demonstrate various combinations of extracted key features; 2) the use of the RS-decisional LEM2 algorithm as effective evaluation approaches for measuring various model combinations. To make further verification, a real dataset was practically collected from the web-multimedia data from the called BANK-CREDIT database. The target dataset contains of 1, 327 samples from Asian banks for credit ratings from 1993-2007. Our research results indicate that the model proposed in this study has a more outperformance than listing models for the standard of accuracy and standard deviation, which indicates clearly model superiority and suitability.