Competitive interaction reasoning: A bio-inspired reasoning method for fuzzy rule based classification systems

In designing fuzzy rule based classification systems (FRBCSs), complex fuzzy rule extraction techniques and tuning membership functions are frequently used to enhance classification accuracy. However, these approaches not only decrease system's transparency which is the hallmark of fizzy design, but also are oftentimes computationally expensive and require multiple parameters to be optimized using a large amount of training data. In this paper, inspired by the so-called competitive behavior of mini-columns in the brain neuronal circuitry, we proposed a new reasoning method for fuzzy classifiers referred to as Competitive Interaction Reasoning (CIR) that employs the cumulative information provided by all fuzzy rules and adjusts the decision boundaries as if the membership functions are directly modified. This mechanism is mathematically implemented by a linear transformation and resembles the competitive interaction observed in brain neuronal columns. Cross-rule competition weights are optimized using Hebbian reinforcement learning. Using a large number of simulations on benchmark data sets, we show that the proposed CIR significantly improves classification accuracy without compromising interpretability of the fuzzy classifier. In addition, CIR can further facilitate formation of the fuzzy rules and incorporation of the expert knowledge by confining the destructive effects of noisy rules or expert inconsistencies. Experiments on 23 well-known benchmark data sets confirm high performance of CIR in comparison with a number of popular classifiers.