An Intelligent System for Business Data Mining
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 fi...
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People & Global Business Association (P&GBA)
2017-06-01
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doaj-74434606b7c244deba84b8277311f8ca2021-02-10T03:35:28ZengPeople & Global Business Association (P&GBA)Global Business and Finance Review 1088-69312384-16482017-06-012221710.17549/gbfr.2017.22.2.1An Intelligent System for Business Data MiningShian-Chang Huang0Tung-Kuang Wu1Nan-Yu Wang2National Changhua University of Education, TaiwanNational Changhua University of Education, TaiwanTa Hwa University of Science and Technology, TaiwanMining 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.http://www.gbfrjournal.org/pds/journal/thesis/20170629084628-AMTGC.pdfbusiness data mininggeneralized discriminant analysisfinancial statementsmultiple kernel machinesupport vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shian-Chang Huang Tung-Kuang Wu Nan-Yu Wang |
spellingShingle |
Shian-Chang Huang Tung-Kuang Wu Nan-Yu Wang An Intelligent System for Business Data Mining Global Business and Finance Review business data mining generalized discriminant analysis financial statements multiple kernel machine support vector machine |
author_facet |
Shian-Chang Huang Tung-Kuang Wu Nan-Yu Wang |
author_sort |
Shian-Chang Huang |
title |
An Intelligent System for Business Data Mining |
title_short |
An Intelligent System for Business Data Mining |
title_full |
An Intelligent System for Business Data Mining |
title_fullStr |
An Intelligent System for Business Data Mining |
title_full_unstemmed |
An Intelligent System for Business Data Mining |
title_sort |
intelligent system for business data mining |
publisher |
People & Global Business Association (P&GBA) |
series |
Global Business and Finance Review |
issn |
1088-6931 2384-1648 |
publishDate |
2017-06-01 |
description |
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. |
topic |
business data mining generalized discriminant analysis financial statements multiple kernel machine support vector machine |
url |
http://www.gbfrjournal.org/pds/journal/thesis/20170629084628-AMTGC.pdf |
work_keys_str_mv |
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