Ab. Aziz, Azizi and Siraj, Fadzilah and Zakaria, Azizi (2002) Development of an adaptive business insolvency classifier prototype (AVICENA) using hybrid intelligent algorithms. In: Student Conference on Research and Development (SCOReD 2002), 2002.

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Abstract

Confronted by an increasingly competitive environment and chaotic economic conditions, businesses are faced with the need to accept greater risk.Businesses do not become insolvent overnight, rather creditors, investors and the financial community will receive either direct or indirect indications that a company is experiencing financial distress.Thus, this paper analyzed the ability of AVICENA to classify business insolvency performance events.Neural networks (multilayer perceptron-backpropagation) serves as a classifier mechanism while a priori algorithms (auto association rules) support the decision made by the neural networks, in which rules are generated.The conventional model for predicting business performance, the Altman-Z scores model, is used for performance comparison.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Print ISBN: 0-7803-7565-3
Uncontrolled Keywords:	Neural Networks, Business Insolvency, Apriori Algorithms, Discriminant Analysis, Financial Ratios.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Dr. Azizi Ab Aziz
Date Deposited:	23 Oct 2014 02:08
Last Modified:	23 Oct 2014 02:08
URI:	https://repo.uum.edu.my/id/eprint/12329

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