Feature selection with a budget

Feature selection is an important step in all practical applications of pattern recognition. As such, it is not surprising that during the past decades it has received a lot of attention from the research community. The topic is well understood and many methods have been put to the test. Most methods, however, overlook an aspect critical to real-time applications: limited computation time. The set of selected features must not only be suitable to solve the task, but must also ensure that the task can be solved within the available time. With this in mind, we propose a method for feature selection with a budget. We approach the problem by stating feature selection as a multi-objective optimization problem. This problem is solved using the well known NSGA-II algorithm. We evaluate our approach using one synthetic and two real-world datasets. We explore the properties of the genetic algorithm and investigate the classification performance of the resulting selections. Our results show that the selected feature sets are highly suitable, especially when considering real-time systems.