Imputation of missing values for semi-supervised data using the proximity in random forests
by Tsunenori Ishioka
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 8, No. 2, 2013

Abstract: This paper presents a procedure that imputes missing values by using random forests on semi-supervised data. Applying our method to Hewlett-Packard Lab.'s spam data and Edgar Anderson's iris data, we found that the rate of correct classification is higher than that of other methods: a simple expansion of Liaw's 'rfImpute' for (un)supervised data and the k-nearest neighbour method (kNN). Our method allows missing predictor variables as well as missing response variable. An imputation that uses random forests for semi-supervised cases in the training dataset has never been implemented until now.

Online publication date: Sat, 28-Jun-2014

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