Instance Selection Approach for Self-Configuring Evolutionary Fuzzy Rule Based Classification Systems

We propose an instance selection technique for a hybrid self-configuring fuzzy evolutionary algorithm to decrease the computation time and increase the accuracy. The classification algorithm used implements both Pitts burg and Michigan approaches to generate fuzzy rules. The instance selection method changes the training sample every fixed number of generations, or adaptation periods. The instances of the training sample are assigned probabilities depending on how are they used and how successfully are they classified. The change in probabilities guides the learning algorithm towards problematic areas of the feature space to generate rule bases, that may appear to be more accurate on the whole dataset. The best solution for the whole training sample is kept independently and always included into the population. We demonstrate that this approach decreases the computation time depending on the size of the selected sub sample. We also test the algorithm for different length of the adaptation period. Results of numerical experiments show the usefulness of the approach developed.