Development of an adaptive business insolvency clasifier prototype (AVICENA) using hybrid intelligent
Confronted by an increasingly competitive environment and chaotic economy conditions. Businesses are facing with the need to accept greater risk. Businesses do not become insolvent overnight,rather many times creditors, investors and the financial community will receive either direct or indirect ind...
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| 主要な著者: | , , |
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| フォーマット: | Book Section |
| 言語: | English |
| 出版事項: |
IEEE Computer Society
2002
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| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/1531/1/Azizi_Ab._Aziz.pdf http://repo.uum.edu.my/1531/ http://ieeexplore.ieee.org/ |
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| 要約: | Confronted by an increasingly competitive environment and chaotic economy conditions. Businesses are facing with the need to accept greater risk. Businesses do not become insolvent overnight,rather many times creditors, investors and the financial community will receive either direct or indirect indrcations that a company is experiencing financial distress. Thus, this paper analyzed the ability of
AVICENA in classifying business insolvency performance events. Neural networks (Multi layer Perceptron - Backpropagation) setves as a classifier mechanism while Apriori algorithms (Auto Association Rules) supports the decision made by the neural networks, in which rules are generated The conventional model in predicting business
performances, called as Altman- Z Scores model is used for performance comparison. |
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