Input and Structure Selection for Κ-Nn Approximator

Abstract

This Paper Presents K-Nn as an Approximator for Time Series Prediction Problems. the Main Advantage of This Approximator is its Simplicity. Despite the Simplicity, &-Nn Can Be Used to Perform Input Selection for Nonlinear Models and It Also Provides Accurate Approximations. Three Model Structure Selection Methods Are Presented: Leave-One-Out, Bootstrap and Bootstrap 632. We Will Show that Both Bootstraps Provide a Good Estimate of the Number of Neighbors, K, Where Leave-One-Out Fails. Results of the Methods Are Presented with the Electric Load from Poland Data Set. © Springer-Verlag Berlin Heidelberg 2005.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Bootstrap; k-NN; Leave-one-out; Model Structure Selection; Time Series Prediction

International Standard Serial Number (ISSN)

0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Jan 2005

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