Applying Feature Selection Combination-Based Rough Set Classifiers to Forecast Credit Rating Status

When banks experience financial scandals or insolvency, panic typically ensues and, in the worst case, leads to systemic banking crises, they are clearly vital to financial market stability, particularly large bank. Therefore, developing an indicator that represents the financial status and operational competence of Asian banks is urgently needed for parties interested in investing in Asia. This study proposes a stepped model that first organizes random forest (RF) and reducts and core of rough set exploration system (RSES) to construct various combinations of extracted key attributes for reducing data dimensions. Accordingly, the rough set LEM2 algorithm is employed as evaluation method to test the various combinations. for verification, a practical dataset comprising 1,327 samples is collected from the BANKSCOPE database, comprising Asian banks covered the period 1993¡V2007. the experimental results indicate that the proposed model outperforms the listing models in terms of accuracy and its standard deviation.