A reinforced fuzzy ARTMAP model for data classification

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a hybrid model consisting of fuzzy ARTMAP (FAM) and reinforcement learning (RL) for tackling data classification problems. RL is used as a feedback mechanism to reward the prototype nodes of data samples established by FAM. Specifically, Q-learning is adopted to develop the hybrid model known as QFAM. A Q-value is assigned to each prototype node, which is updated incrementally based on the prediction accuracy of the node pertaining to each data sample. To evaluate the performance of the proposed QFAM model, a series of experiments with benchmark problems and a real-world case study, i.e., human motion recognition, are conducted. The bootstrap method is used to quantify the results with the 95% confidence interval estimates. The results are also compared with those from FAM as well as other models reported in the literature. The outcomes indicate the effectiveness of QFAM in tackling data classification tasks.