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A New Efficient and Effective Fuzzy Modeling Method for Binary Classification

A New Efficient and Effective Fuzzy Modeling Method for Binary Classification

T. Warren Liao
Copyright: © 2013 |Pages: 19
ISBN13: 9781466618701|ISBN10: 1466618701|EISBN13: 9781466618718
DOI: 10.4018/978-1-4666-1870-1.ch004
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MLA

Liao, T. Warren. "A New Efficient and Effective Fuzzy Modeling Method for Binary Classification." Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization, edited by Toly Chen, IGI Global, 2013, pp. 41-59. https://doi.org/10.4018/978-1-4666-1870-1.ch004

APA

Liao, T. W. (2013). A New Efficient and Effective Fuzzy Modeling Method for Binary Classification. In T. Chen (Ed.), Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization (pp. 41-59). IGI Global. https://doi.org/10.4018/978-1-4666-1870-1.ch004

Chicago

Liao, T. Warren. "A New Efficient and Effective Fuzzy Modeling Method for Binary Classification." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization, edited by Toly Chen, 41-59. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-1870-1.ch004

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Abstract

This paper presents a new fuzzy modeling method that can be classified as a grid partitioning method, in which the domain space is partitioned by the fuzzy equalization method one dimension at a time, followed by the computation of rule weights according to the max-min composition. Five datasets were selected for testing. Among them, three datasets are high-dimensional; for these datasets only selected features are used to control the model size. An enumerative method is used to determine the best combination of fuzzy terms for each variable. The performance of each fuzzy model is evaluated in terms of average test error, average false positive, average false negative, training error, and CPU time taken to build model. The results indicate that this method is best, because it produces the lowest average test errors and take less time to build fuzzy models. The average test errors vary greatly with model sizes. Generally large models produce lower test errors than small models regardless of the fuzzy modeling method used. However, the relationship is not monotonic. Therefore, effort must be made to determine which model is the best for a given dataset and a chosen fuzzy modeling method.

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