On the Selection of Fuzzy Classifiers Using AdaBoost

We present a novel framework for pattern classification. The training phase is a two step process. In the first step a number of simple Fuzzy Inference Engines (FIEs) are constructed to perform classification based on linguistic rules for weak learner score interpretation. The linguistic rules are simple if-then-else type conditions imposed on the weak learner scores combined with various membership functions and logical AND-OR-NOT type operators. In the next step the AdaBoost algorithm is used to find a reduced set of fuzzy engines from a pool of FIEs. The detection rate and false positive rate on face detection data have been found to be comparable to other popular face detection algorithms. The processing time for each pattern is constrained only by the time taken by the input weak learner; the FIE always takes the same amount of processing time irrespective of the size of the image.