An intelligent system for business data mining

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalGlobal Business and Finance Review
Volume22
Issue number2
DOIs
Publication statusPublished - 2017 Jan 1

Fingerprint

Data mining
Discriminant analysis
Kernel
Intelligent systems
Prediction
Bankruptcy prediction
Predictors
Financial statements
Operator
Financial information
Nonlinearity
Classifier
Stock market
Financial data

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Finance

Cite this

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abstract = "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.",
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An intelligent system for business data mining. / Huang, Shian-Chang; Wu, Tung-Kuang; Wang, Nan Yu.

In: Global Business and Finance Review, Vol. 22, No. 2, 01.01.2017, p. 1-7.

Research output: Contribution to journalArticle

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