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Imputation of Missing Values for Unsupervised Data Using the Proximity in Random Forests

Authors:
Tsunenori Ishioka

Keywords: Ensemble learning; k-nearest neighbor; R; rfImpute; impute.knn

Abstract:
This paper presents a new procedure that imputes missing values by random forests for unsupervised data. We found that it works pretty well compared with k-nearest neighbor (kNN) and rough imputations replacing the median of the variables. Moreover, this procedure can be expanded to semi-supervised data sets. The rate of the correct classification is higher than that of other conventional methods. The imputation by random forests for unsupervised or semi-supervised cases was not implemented.

Pages: 30 to 36

Copyright: Copyright (c) IARIA, 2013

Publication date: February 24, 2013

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-61208-253-0

Location: Nice, France

Dates: from February 24, 2013 to March 1, 2013