Addressing Accuracy Issues of Fuzzy Cognitive Map-Based Classifiers

The chapter presents an FCM-based model for pattern classification termed Long-Term Cognitive Network (LTCN). This model uses the class-per-output architecture discussed in the previous chapter and the quasi-nonlinear reasoning rule to avoid the unique fixed-point attractor. To improve its prediction capabilities, the LTCN-based classifier suppresses the constraint that weights must be in the [–1, 1] interval while using all temporal states produced by the network in the classification process. As for the tuning aspect, this classifier is equipped with two versions of the Moore-Penrose learning algorithm. Besides presenting the mathematical formalism of this model and its ensuing learning algorithm, we will develop an example that shows the steps required to solve classification problems using an existing Python implementation. The chapter also elaborates on a measure that estimates the role of each concept in the classification process and presents simulation results using real-world datasets. After reading this chapter, the reader will have acquired a solid understanding of the fundamentals of these algorithms and will be able to apply them to real-world pattern classification datasets.