HSIM: A Supervised Imputation Method for Hierarchical Classification Scenario

Abstract

The missing value imputation process can be defined as a preprocessing step that fills missing values of attributes in incomplete datasets. Nowadays, the problem of incomplete datasets in the hierarchical classification scenario must be solved using unsupervised missing value imputation methods due to the lack of supervised methods to deal with the hierarchical context. Thus, in this work, we propose and evaluate a supervised missing value imputation method for datasets used in hierarchical classification problems in which the classes are organized into tree structure. Experiments were performed on incomplete datasets to evaluate the effect of the proposed missing value imputation method on classification performance when using a global hierarchical classifier. The results showed that, using the proposed method for dealing with missing attribute values, it provided higher classifier predictive performance than other unsupervised missing value imputation methods.