A projection based learning algorithm for Meta-Cognitive Neuro-Fuzzy Inference system

In this paper, we propose a Projection Based Learning (PBL) algorithm for a Meta-Cognitive Neuro-Fuzzy Inference (McFIS) together referred to as PBL-McFIS. McFIS consists of a cognitive component, which is a zero-order Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system, and a meta-cognitive component, which is a self-regulatory learning mechanism for the neuro-fuzzy inference system. The learning in the cognitive component begins with zero rules, and as new samples are presented to the network, the meta-cognitive component monitors the hinge-loss error and spherical potential of the current sample to efficiently decide on what-to-learn, when-to-learn and how-to-learn. In this work we employ PBL-McFIS to solve classification problems and hence the monitory signals employ class-specific self-adaptive thresholds to decide on efficient learning strategies. These thresholds are self-adapted such that the trained network is compact and avoids over-fitting. During addition of new rules or updating of existing rules, the optimal output weights corresponding to the minimum hinge-loss error is computed using PBL algorithm. The learning algorithm considers class-specific as well as class overlap factors during training. The performance of PBL-McFIS is evaluated on a set of benchmark classification problems. The statistical performance analysis with other state-of-the-art neuro-fuzzy inference systems and SVM indicate improved classification ability of the proposed algorithm.