A probabilistic fuzzy learning system for pattern classification
Geng Zhang, Han‐Xiong Li · 2010
There always exist stochastic and fuzzy uncertainties in the real-world. In this paper, the probabilistic fuzzy theory is used to construct a probabilistic fuzzy classifier for the pattern classification under these two uncertainties. By properly designing the secondary probability density function and the probabilistic fuzzy inference, and with a probabilistic voting method introduced, the probabilistic fuzzy classifier can achieve a better performance than that of the traditional fuzzy method or the pure probabilistic method. Moreover, probabilistic fuzzy rules extracted from expert knowledge or the process data will make the decision more realistic and easy to understand. The probabilistic property embedded in the data can be considered as the confidence level of the decision, which is impossibly shown in the traditional fuzzy classification. Finally, the experiment results have demonstrated that the advantages of the proposed PFC under the complex stochastic environment.