This paper proposes a novel two-stagefuzzyclassificationmodel established by the fuzzy feature extraction agent (FFEA) and the fuzzyclassification 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 fuzzymodels 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 fuzzyclassificationmodel. 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 fuzzyclassificationmodel can provide a sufficiently high classification rate in comparison with other models in the literature.
關聯:
Expert Systems with Applications, no.35, pp.1482-1495
