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WEBIC: a web based business insolvency classifier using neural networks


Siraj, Fadzilah and Zakaria, Azizi and Ab. Aziz, Azizi and Abas, Zulhazlin (2003) WEBIC: a web based business insolvency classifier using neural networks. In: Malaysian-Japan Seminar on Artificial Intelligence Applications in Industry, 24-25 June 2003, Park Plaza Hotel, Kuala Lumpur.. (Unpublished)

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Abstract

Business insolvency is one of the major problems faced by decision makers, especially to detect the early symptom that may contribute to critical business condition.This paper discusses the implementation of neural networks in classifying business insolvency cases in Malaysia. The developed prototype can be accessed remotely via World Wide Web (WWW).For the development purposes, the data was obtained from the Registrar of Business / Companies (ROB/ROC), Kuala Lumpur Stock Exchange and Bank Negara Malaysia (Central Bank of Malaysia).Several experiments were conducted to determine the most suitable parameters for the neural network model.Based on the experimental results, a network with an architecture of 11-6-1 with learning rate 0.1 and momentum term of 0.5. The prototype obtained 90.25% generalization and therefore indicates that the prototype has the potential to be used as a tool for classifying business insolvency.Hence, the prototype provides a basic framework for developing such a classifier

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Business Insolvency, Classification, Decision Support, Neural Networks.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Prof Madya Fadzilah Siraj
Date Deposited: 01 Feb 2017 02:59
Last Modified: 01 Feb 2017 02:59
URI: http://repo.uum.edu.my/id/eprint/20816

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