Imputation of Missing Values for Unsupervised Data Using the Proximity in Random Forests
نویسنده
چکیده
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 semisupervised 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. Keywords-Ensemble learning; k-nearest neighbor; R; rfImpute; impute.knn.
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تاریخ انتشار 2013