Boosting the performance of the fuzzy min-max neural network in pattern classification tasks

In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.