Three improved fuzzy lattice neurocomputing (FLN) classifiers
Al Cripps, Nhan T. Nguyen, Vassilis G. Kaburlasos · 2004
Three novel fuzzy lattice neurocomputing (FLN) classifiers, namely FLN first fit (FLNff), FLN ordered tightest fit (FLNotf), and FLN selective fit (FLNsf), are introduced in this work. Learning is incremental, memory-based, data order dependent, and polynomial O(n/sup 3/) where n is the number of the training data. Convenient geometric interpretations on the plane illustrate the mechanics of the aforementioned FLN classifiers whose capacity is demonstrated in three benchmark classification problems. The classification results compare favorably with the results by alternative classification methods from the literature. In addition, an FLN classifier can both induce rules from the data and it can deal with numeric and/or nominal data including missing attribute values. An important experimental outcome of this work is that the computation of "smaller than maximal" lattice intervals can increase considerably the capacity for generalization.