A combining strategy for ill-defined problems

In this paper we present a combining strategy to cope with the problem of classification in ill-defined domains. In these cases, even though a particular target class may be sampled in a representative manner, an outlier class may be poorly sampled, or new outlier classes may occur that have not been considered during training. This may have a considerable impact on classification performance. The objective of a classifier in this situation is to utilise all known information in discriminating, and to remain as robust as possible to changing conditions. A classification scheme is presented that deals with this problem, consisting of a sequential combination of a one-class and multi-class classifier. We show that it can outperform the traditional classifier with reject-option scheme, locally selecting/training models for the purpose of optimising the classification and rejection performance.