A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems

Abstract

In this paper, we present a metacognitive sequential learning algorithm for a neuro-fuzzy inference system for classification tasks, which is referred to as a “metacognitive neuro-fuzzy inference system (McFIS).” The McFIS learning algorithm is developed based on the principles of the best human learning strategy, viz., a self-regulatory learning strategy in a metacognitive framework. McFIS has two components: a cognitive component and a metacognitive component. A neuro-fuzzy inference system forms the cognitive component of the McFIS, and a self-regulatory learning mechanism forms its metacognitive component. The learning ability of the cognitive component is monitored and controlled by the self-regulatory learning mechanism. For each sample in the training dataset, the metacognitive component uses its self-adaptive thresholds to choose one of the following learning strategies based on the criteria that depends on class-specific knowledge: 1) sample deletion; 2) sample learning; and 3) sample reserve. Thus, the metacognitive component decides what-to-learn, when-to-learn, and how-to-learn the training samples. When a new rule is added, the parameters of the new rule are assigned such that the rule has minimum overlapping with the adjacent rules as well as the localization property of the Gaussian rules is efficiently exploited. Performance of the McFIS is evaluated using several well-known benchmark multicategory/binary classification datasets from the University of California, Irvine machine learning repository and on a practical human action recognition problem. The results clearly indicate that the proposed metacognitive learning helps the McFIS achieve better performance than other existing classifiers.