Optimum Feature Ordering for Dynamic Instance-Wise Joint Feature Selection and Classification

Abstract

We introduce a supervised machine learning framework to perform joint feature selection and classification individually for each data instance during testing. In contrast to our prior work, we decide both the order and the number of features for each data instance. Specifically, our proposed solution dynamically selects the feature to review at each stage based on the already observed features and stops the selection process to make a prediction once it determines no classification improvement can be achieved. To gain insights, we analyze the properties of the proposed solution. Based on these properties, we propose a fast algorithm and demonstrate its effectiveness compared to the state–of–the–art using 4 publicly available datasets.