PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimal Pruned K-Nearest Neighbors: OP-KNN - Application to Financial Modeling
Qi Yu, Antti Sorjamaa, Yoan Miche, Amaury Lendasse, Alberto Guillén, Eric Séverin and Fernando Mateo
In: HIS 2008, 8th International Conference on Hybrid Intelligent Systems, September 10-12 2008, Barcelona, Spain.

Abstract

The paper proposes a methodology called OP-KNN, which builds a one hidden- layer feedforward neural net- work, using nearest neighbors neurons with extremely small computational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels. Multiresponse Sparse Regression (MRSR) is used as the second step in order to rank each kth nearest neighbor and finally as a third step Leave-One-Out estima- tion is used to select the number of neighbors and to esti- mate the generalization performances. This new methodol- ogy is tested on a toy example and is applied to financial modeling.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4782
Deposited By:Amaury Lendasse
Deposited On:24 March 2009