Increasing the Performance of Fuzzy-Rough Cognitive Networks

Abstract

Pattern classification is a popular research field within the Machine Learning discipline. Black-box models have proven to be potent classifiers in this particular field. However, their inability to provide a transparent decision mechanism is often regarded as an undesirable feature. Fuzzy-Rough Cognitive Networks are granular classifiers that have proven competitive and effective in such tasks. In this paper, we examine the contribution of the FRCN's main building blocks, being the causal weight matrix and the activation values of the neurons, to the model's average performance. Noise injection is employed to this end. Furthermore, we explore various alternatives for the current structure of these building blocks. Firstly, we experiment with possible ways of adjusting the weight matrix, which is originally composed of fixed weight values based on set rules. Secondly, we explore if computing a confidence degree per decision class from another, potentially weaker, classifier could lead to more powerful neuron activation and possibly an improved performance.