A Single Nearest Neighbor Fuzzy Approach for Pattern Recognition

single nearest neighbour fuzzy approach 2 ABSTRACT The main aim of this paper is to introduce the single nearest neighbour approach for pattern recognition and the concept of incremental learning of a fuzzy classifier where decision making is based on data available up to time t rather than what may be available at the start of the trial, i.e. at t=0. The single nearest neighbour method is explained in the context of solving the classic two spiral benchmark. The proposed approach is further tested on the electronic nose coffee data to judge its performance on a real problem. This paper illustrates: 1) a novel fuzzy classifier system based on the single nearest neighbour method; 2) its application to the spiral benchmark taking the incremental pattern recognition approach; and 3) results obtained when solving the two spiral problem with both non-incremental and incremental methods and coffee classification with the non-incremental method. The results show that incremental learning leads to improved recognition performance for spiral data and it is possible to study the behavioural characteristics of the classifier with possibility related parameters.