Combining one-class classifiers

Abstract

In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is as- sumed that only information of the target class is available while noth- ing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the classifiers with poorer perfor- mance might waste valuable information. To improve performance the results of different classifiers (which may difier in complexity or training algorithm) can be combined. This can not only increase the performance but it can also increase the robustness of the classification. Because for one-class classifiers only information of one of the classes is present, com- bining one-class classifiers is more dificult. In this paper we investigate if and how one-class classifiers can be combined best in a handwritten digit recognition problem.