@misc{10481/65128,
year = {2013},
url = {http://hdl.handle.net/10481/65128},
abstract = {The Nearest Neighbor (NN) algorithm is a well-known and effective classification algorithm. Prototype Selection (PS), which provides NN with a good training set to pick its neighbors from, is an important topic as NN is highly susceptible to noisy data. Accurate state-of-the-art PS methods are generally slow, which motivates us to propose a new PS method, called OWA-FRPS. Based on the Ordered Weighted Average (OWA) fuzzy rough set model, we express the quality of instances, and use a wrapper approach to decide which instances to select. An experimental evaluation shows that OWA-FRPS is significantly more accurate than state-of-the-art PS methods without requiring a high computational cost.},
organization = {Spanish Government
	
TIN2011-28488},
publisher = {Springer Nature},
keywords = {Ordered Weighted Average},
keywords = {Fuzzy rough set},
keywords = {Prototype selection},
keywords = {KNN},
title = {OWA-FRPS: A Prototype Selection method based on Ordered Weighted Average Fuzzy Rough Set Theory},
doi = {10.1007/978-3-642-41218-9_19},
author = {Verbiest, Nele and Cornelis, Chris and Herrera Triguero, Francisco},
}