A transparent classification model using a hybrid soft computing method

Ainon, R.N. and Lahsasna, A. and Wah, T.Y. (2009) A transparent classification model using a hybrid soft computing method. In: 3rd Asia International Conference on Modelling and Simulation, MAY 25-29, 2009, Bundang, INDONESIA .

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Due to the inherent complexity of many real-world problems, classification models have become an important tool for solving pattern recognition tasks in many disciplines such as medicine, finance and management. Accuracy and transparency are two important criteria that should be satisfied by any classification model. In this paper, a transparent and relatively accurate classifier is developed using a hybrid soft computing technique. The initial fuzzy model is first generated using a clustering method and the transparency and accuracy of the model are then simultaneously optimized using a multi-objective evolutionary technique. The proposed model is tested on two real problems; the first one is related to credit scoring problem while the other is on medical diagnosis. All the data sets used in this study are publicly available at UCI repository of machine learning database.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia
Uncontrolled Keywords: Fuzzy Systems; Transparency; Genetic Algorithms
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Mr. Faizal Hamzah
Date Deposited: 24 Oct 2011 03:29
Last Modified: 24 Oct 2011 03:29
URI: http://eprints.um.edu.my/id/eprint/2261

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