Reducing Missingness in a Stream through Cost-Aware Active Feature Acquisition
Maik Büttner, Christian Beyer, Myra Spiliopoulou · 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) · 2022
Missing features can negatively impact the performance of machine learning solutions and past research has been focused on how to acquire the most predictive features in static scenarios. Active Feature Acquisition (AFA) for data streams extends the conventional static paradigm by taking into account that the importance of a feature may change as the stream drifts.In this study, we propose an AFA method that takes the cost of the features for each arriving instance into account and, at the same time, allows multiple features to be acquired at once. This reflects the fact that labels often depend on multiple features, so that acquiring many low-cost features may result in higher improvement than acquiring a single feature, as has been proposed in our earlier work [1]. We evaluated our approach on 7 real and 8 synthetic data sets. We investigated three different budget sizes and three different feature cost configurations for 7 different percentages of missing data on each of our data sets. We use the results of our experiments to elaborate on when the acquisition of multiple features is more or less beneficial than acquiring a single feature. Finally, we show the need for more sophisticated metrics to estimate a feature’s predictive quality.