Dynamic Feature Selection from Variable Feature Sets Using Features of Features
This addresses a practical limitation in cost-sensitive machine learning for real-world applications where feature availability is inconsistent, though it appears incremental by extending existing dynamic feature selection methods.
The paper tackles the problem of dynamic feature selection when the set of measurable features varies per instance, proposing a deep learning method that uses 'features of features' to reduce measurement costs, with experimental results showing effective feature selection across several datasets.
Machine learning models usually assume that a set of feature values used to obtain an output is fixed in advance. However, in many real-world problems, a cost is associated with measuring these features. To address the issue of reducing measurement costs, various methods have been proposed to dynamically select which features to measure, but existing methods assume that the set of measurable features remains constant, which makes them unsuitable for cases where the set of measurable features varies from instance to instance. To overcome this limitation, we define a new problem setting for Dynamic Feature Selection (DFS) with variable feature sets and propose a deep learning method that utilizes prior information about each feature, referred to as ''features of features''. Experimental results on several datasets demonstrate that the proposed method effectively selects features based on the prior information, even when the set of measurable features changes from instance to instance.