Instance-wise Feature Acquisition with Classifier Selection Option for Structured Data Instances

Abstract

We propose a method that sequentially acquires features and selects a classifier for label assignment in data instances of related variables. The objective is to accurately infer the values of such variables (labels), ensuring at the same time that the expected total feature acquisition cost is minimum. To this end, building upon our prior work, our proposed method selects to employ one out of a set of available classifiers after completing the feature acquisition stage. The resulting labels are propagated through the known Bayesian network, which captures the relationships between the variables, and used during the label assignment of the remaining variables. We assess the performance of our method using five datasets, and observe that using classifiers in an instance–wise fashion improves accuracy, but also leads to acquiring less features on average.