A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems

A neuro-fuzzy classifier based on the meta-cognitive principle of human self-regulated learning (Mc-FIS) is proposed in this paper. The network decides what-to-learn, when-to-learn and how-to-learn based on the current information present in the classifier and the new information present in the sample. The classifier utilizes self-regulating error based criterion to decide which sample to learn and when to learn. A rule is pruned if its significance is below a particular threshold, based on class specific information. This results in a compact network and sample deletion helps overfitting. Class specific information is used in executing the above tasks. The algorithm is evaluated on balanced and unbalanced benchmark problems from UCI machine learning repository. The results clearly indicate the superiority of the developed algorithm.