Adversarial feature selection
Karan Kumar Budhraja, Tim Oates · 2015
This work introduces adversarial feature selection, a game between a feature selection agent and its adversary. The adversarial approach is drawn from existing work on adversarial classification. The feature selection algorithm selects a subset of features from the original set based on their utility towards classification accuracy. A cost is incurred based on features selected. The adversary modifies features with less utility as ones with higher utility. In this way, the adversary profits if the feature selector selects these features at increased costs. A base feature selection algorithm is used to generate cost values for features. The problem is formalized and a corresponding model of the algorithm is discussed and evaluated.