OWA-FRPS: A Prototype Selection method based on Ordered Weighted Average Fuzzy Rough Set Theory Verbiest, Nele Cornelis, Chris Herrera Triguero, Francisco Ordered Weighted Average Fuzzy rough set Prototype selection KNN 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. 2020-12-23T10:07:43Z 2020-12-23T10:07:43Z 2013 conference output Published version: Verbiest N., Cornelis C., Herrera F. (2013) OWA-FRPS: A Prototype Selection Method Based on Ordered Weighted Average Fuzzy Rough Set Theory. In: Ciucci D., Inuiguchi M., Yao Y., Ślęzak D., Wang G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science, vol 8170. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/978-3-642-41218-9_19] http://hdl.handle.net/10481/65128 10.1007/978-3-642-41218-9_19 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ open access Atribución-NoComercial-SinDerivadas 3.0 España Springer Nature