OP-KNN: Method and Applications. (24th March 2010)

• Record Type: Journal Article
• Authors: Yu, Qi; Miche, Yoan; Sorjamaa, Antti; Guillen, Alberto; Lendasse, Amaury; Séverin, Eric
• Other Names: Chen Songcan Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2010)
• Publication Date: 2010-03-24
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2010/597373
• Languages: English
• ISSNs: 1687-7594

Abstract

Abstract : This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each k th nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed.