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OP-KNN: Theory and Applications
Qi Yu, Yoan Miche, Antti Sorjamaa, Alberto Guillen, Amaury Lendasse and Eric Severin
Advances in Artificial Neural Systems
2010.
AbstractThis paper presents a methodology named Optimally Pruned
K-Nearest Neighbors (OP-KNN) which as the advantage of competing
with state of the art methods while remaining fast. It builds a one hiddenlayer
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 characteristics
of this method is that it provides good performance and a comparatively
simple model at extremely high learning speed. [Edit]
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