Improved Experimental Results Using Fuzzy Lattice Neurocomputing (FLN) Classifiers

This work shows comparatively the capacity of five Fuzzy Lattice Neurocomputing (FLN) classifiers. The mechanics of the five classifiers are illustrated geometrically on the plane. Both learning and generalization are based on the computation of hyperboxes in space RN. Learning is memory-based, and polynomial O(n3) where n is the number of the training data. The problem of overfitting is ruled out by construction. In addition, a FLN classifier both induces rules from the training data and it is applicable beyond RN, in particular a FLN classifier is applicable in a mathematical lattice data domain hence disparate types of data can be dealt with in principle. Experimental results in three benchmark classification problems involving data sets of various sizes and various types, i.e. numerical/nominal data, compare favorably with the results by alternative classification methods from the literature. Various theoretical advantages are discussed.