Genetically Evolved Fuzzy Rule-Based Classifiers and Application to Automotive Classification

Type-2 fuzzy logic systems (FLSs) have been treated as a magic black box which can better handle uncertainties due to the footprint of uncertainty (FOU). Although the results in control applications are promising, the advantages of type-2 framework in fuzzy pattern classification is still unclear due to different forms of outputs produced by both systems. This paper aims at investigating if type-2 fuzzy classifier can deliver a better performance when there exists imprecise decision boundary caused by improper feature extraction method. Genetic Algorithm (GA) is used to tune the fuzzy classifiers under Pittsburgh scheme. The proposed fuzzy classifiers have been successfully applied to an automotive application whereby the classifier needs to detect the presence of human in a vehicle. Results reveal that type-2 classifier has the edge over type-1 classifier when the decision boundaries are imprecise and the fuzzy classifier itself has not enough degrees of freedom to construct a suitable boundary. Conversely, when decision boundaries are clear, the advantage of type-2 framework may not be significant anymore. In any case, the performance of a type-2 fuzzy classifier is at least comparable with a type-1 fuzzy classifier. When dealing with real world classification problem where the uncertainty is usually difficult to be estimated, type-2 fuzzy classifier can be a more rational choice.