Analysis and design of rank-based classifiers

In this paper, we propose a method to predict the presence or absence of correct classification results in classification problems with many classes and the output of the classifier is provided in the form of a ranking list. This problem differs from the “traditional” classification tasks encountered in pattern recognition. While the original problem of forming a ranking of the most likely classes can be solved by running several classification methods, the analysis presented here is moved one step further. The main objective is to analyse (classify) the provided rankings (an ordered list of rankings of a fixed length) and decide whether the “true” class is present on this list. With this regard, a two-class classification problem is formulated where the underlying feature space is built through a characterization of the ranking lists. Experimental results obtained for synthetic data as well as real world face identification data are presented.

► A method to assess the correctness of the answer of ranking classifier is proposed.
► The analysis whether the true class is present on a given ranking list is proposed.
► A new classification problem is based on a characterization of the ranking lists.
► Experimental results on artificial data as well as on FERET database are presented.