TY - GEN
T1 - A New Approach to Fuzzy-Rough Nearest Neighbour Classification
AU - Jensen, Richard
AU - Cornelis, Chris
N1 - R. Jensen and C. Cornelis. A New Approach to Fuzzy-Rough Nearest Neighbour Classification. Transactions on Rough Sets XIII, LNCS 6499, pp. 56-72, 2011.
PY - 2011
Y1 - 2011
N2 - A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, 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 FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm.
AB - A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, 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 FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm.
M3 - Conference Proceeding (Non-Journal item)
SP - 310
EP - 319
BT - Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
ER -