Active problem workspace reduction with a fast fuzzy classifier for real-time applications

In this paper, a Sequential Fuzzy Indexing Tables classifier is proposed for problems that require fast online operation. Its base idea comes from fuzzy hypermatrices (which are specialized versions of fuzzy look-up tables) that realize nearest-neighbor classification in order to recognize patterns similar to known ones. It is done by mapping the problem space into the memory in form of multidimensional matrices, so the class of the input data can be gained instantly in the evaluation phase. The downside of the base method is that the memory requirements scale exponentially with the number of attributes and the size of the attribute domains. The proposed classifier solves this issue for problems with large, but sparse workspaces by storing only a part of the problem domain. Thus instead of using a single multidimensional matrix, the classifier consists of a layered structure, breaking the multi-dimensional problem to a sequential combination of 2D fuzzy matrices.