Design of a two-stage fuzzy classification model

  • Authors:

  • Tzuu-Hseng S. Li;Nai Ren Guo;Chia Ping Cheng

  • Affiliations:

  • aiRobots Laboratory, Department of Electrical Engineering, National Cheng Kung University, 1, University Road, Tainan 70101, Taiwan, ROC;Department of Electrical Engineering, Tung Fang Institute of Technology, Kaohsiung County 82941, Taiwan, ROC;aiRobots Laboratory, Department of Electrical Engineering, National Cheng Kung University, 1, University Road, Tainan 70101, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if-then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if-then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature.