TY - JOUR
T1 - An intelligent system for business data mining
AU - Huang, Shian Chang
AU - Wu, Tung Kuang
AU - Wang, Nan Yu
PY - 2017
Y1 - 2017
N2 - Mining high-dimensional business data is a challenging problem. Particularly in bankruptcy predictions, we need to analyze large amounts of information from financial statements and stock markets. This paper proposes a new strategy to deal with the problem. Because of the highly correlation among financial information, this study employed a technique called generalized discriminant analysis (GDA) to identify important features and reduce the data dimension. GDA is a nonlinear discriminant analysis using kernel function operator. It’s easy to deal with a wide class of nonlinearity in financial data, and can reduce the computational loading of subsequent prediction classifier. Due to the promising success of kernel machines in many applications, this study utilized a generalized multiple kernel machine (GMKM) to serve as the predictor. Combining the strengths of GDA and GMKM, our system robustly outperforms traditional prediction systems.
AB - Mining high-dimensional business data is a challenging problem. Particularly in bankruptcy predictions, we need to analyze large amounts of information from financial statements and stock markets. This paper proposes a new strategy to deal with the problem. Because of the highly correlation among financial information, this study employed a technique called generalized discriminant analysis (GDA) to identify important features and reduce the data dimension. GDA is a nonlinear discriminant analysis using kernel function operator. It’s easy to deal with a wide class of nonlinearity in financial data, and can reduce the computational loading of subsequent prediction classifier. Due to the promising success of kernel machines in many applications, this study utilized a generalized multiple kernel machine (GMKM) to serve as the predictor. Combining the strengths of GDA and GMKM, our system robustly outperforms traditional prediction systems.
UR - http://www.scopus.com/inward/record.url?scp=85021698063&partnerID=8YFLogxK
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U2 - 10.17549/gbfr.2017.22.2.1
DO - 10.17549/gbfr.2017.22.2.1
M3 - Article
AN - SCOPUS:85021698063
VL - 22
SP - 1
EP - 7
JO - Global Business and Finance Review
JF - Global Business and Finance Review
SN - 1088-6931
IS - 2
ER -