A New Approach to Fuzzy-Rough Nearest Neighbour Classification

Authors:
Richard Jensen;Chris Cornelis
Affiliations:
Dept. of Comp. Sci., Aberystwyth University, Ceredigion, UK SY23 3DB;Dept. of Appl. Math. and Comp. Sci., Ghent University, Gent, Belgium
Venue:
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Year:
2008

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Abstract

In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, as an alternative to Sarkar's fuzzy-rough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the experimental analysis, we evaluate our approach with both classical fuzzy-rough approximations (based on an implicator and a t-norm), as well as with the recently introduced vaguely quantified rough sets. Preliminary results are very good, and in general FRNN outperforms both FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm.