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Imputation of Missing Values for Semi-supervised Data Using the Proximity in Random Forests
| Content Provider | CiteSeerX |
|---|---|
| Author | Ishioka, Tsunenori |
| Abstract | This paper presents a procedure that imputes missing values by using random forests on semi-supervised data. We found that the rate of correct classification of our method is higher than that of other methods: a simple expansion of Liaw’s “rfImpute ” for (un)supervised data and the k-nearest neigh-bor method (kNN). Our method can handle missing predic-tor variables as well as missing response variable. An im-putation that uses random forests for semi-supervised cases in the training data set has never been implemented until now. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
