OP-KNN : Method and Applications
Joint Authors
Guillen, Alberto
Lendasse, Amaury
Miche, Yoan
Yu, Qi
Séverin, Eric
Sorjamaa, Antti
Source
Advances in Artificial Neural Systems
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-03-24
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
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 kth 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.
American Psychological Association (APA)
Yu, Qi& Miche, Yoan& Sorjamaa, Antti& Guillen, Alberto& Lendasse, Amaury& Séverin, Eric. 2010. OP-KNN : Method and Applications. Advances in Artificial Neural Systems،Vol. 2010, no. 2010, pp.1-6.
https://search.emarefa.net/detail/BIM-483894
Modern Language Association (MLA)
Yu, Qi…[et al.]. OP-KNN : Method and Applications. Advances in Artificial Neural Systems No. 2010 (2010), pp.1-6.
https://search.emarefa.net/detail/BIM-483894
American Medical Association (AMA)
Yu, Qi& Miche, Yoan& Sorjamaa, Antti& Guillen, Alberto& Lendasse, Amaury& Séverin, Eric. OP-KNN : Method and Applications. Advances in Artificial Neural Systems. 2010. Vol. 2010, no. 2010, pp.1-6.
https://search.emarefa.net/detail/BIM-483894
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-483894