On the use of classification reliability for improving performance of the one-per-class decomposition method

Typical pattern recognition applications require to handle both binary and multiclass classification problems. Several researchers have pointed out that obtaining a classifier that discriminates between two classes is much easier than building one that simultaneously distinguishes among all classes. This observation has motivated substantial research on using a pool of binary classifiers to address multiclass problems. Such an approach is also named as decomposition method. Anyway, the performance of a given classification system can be sometimes unsatisfactory for the needs of real applications, especially when these are characterized by large data variability and/or significant amount of noise. In these cases it is important that the classification system is able to estimate the reliability of its decision for each sample under test. This estimate could be used, for example, for deciding to reject a sample instead of running the risk of misclassifying it, so improving the overall system performance. Based on these motivations, this paper defines a reliability estimator for decomposition schemes belonging to the One-per-Class framework. The estimator is based on the reliabilities provided by each binary classifier, on the status of their outputs while it is independent of their design. The performance of the proposed approach has been assessed on private and public medical datasets, showing that it can be used to improve the classification performance of the One-per-Class scheme with respect to both multiclass classifiers and other well-known decomposition schemes.