Multi-functional nearest-neighbour classification

The k nearest-neighbour (kNN) algorithm has enjoyed much attention since its inception as an intuitive and effective classification method. Many further
developments of kNN have been reported such as those integrated with fuzzy sets, rough sets, and evolutionary computation. In particular, the fuzzy and
rough modifications of kNN have shown significant enhancement in performance. This paper presents another significant improvement, leading to a multi-functional nearest-neighbour (MFNN) approach which is conceptually
simple to understand. It employs an aggregation of fuzzy similarity relations and class memberships in playing the critical role of decision qualifier to perform
the task of classification. The new method offers important adaptivity in dealing with different classification problems by nearest-neighbour classifiers, due to the large and variable choice of available aggregation methods and similarity metrics. This flexibility allows the proposed approach to be implemented in a variety of forms. Both theoretical analysis and empirical evaluation demonstrate that conventional kNN and fuzzy nearest-neighbour, as well as two recently developed fuzzy-rough nearest-neighbour algorithms can be considered
as special cases of MFNN. Experimental results also confirm that the proposed approach works effectively and generally outperforms many state-of-the-art
techniques