NNRMLR: A combined method of nearest neighbor regression and multiple linear regression

Hideo Hirose, Yusuke Soejima, Kei Hirose

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

To predict the continuous value of target variable using the values of explanation variables, we often use multiple linear regression methods, and many applications have been successfully reported. However, in some data cases, multiple linear regression methods may not work because of strong local dependency of target variable to explanation variables. In such cases, the use of the k nearest-neighbor method (k-NN) in regression can be an alternative. Although a simple k-NN method improves the prediction accuracy, a newly proposed method, a combined method of k-NN regression and the multiple linear regression methods (NNRMLR), is found to show prediction accuracy improvement. The NNRMLR is essentially a nearest-neighbor method assisted with the multiple linear regression for evaluating the distances. As a typical useful example, we have shown that the prediction accuracy of the prices for auctions of used cars is drastically improved.

Original languageEnglish
Title of host publicationProceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
Pages351-356
Number of pages6
DOIs
Publication statusPublished - Dec 14 2012
Externally publishedYes
Event1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012 - Fukuoka, Japan
Duration: Sep 20 2012Sep 22 2012

Publication series

NameProceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012

Other

Other1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
CountryJapan
CityFukuoka
Period9/20/129/22/12

All Science Journal Classification (ASJC) codes

  • Information Systems

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