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A typical recognition system consists of a sequential combination of two experts, called a detector and classifier respectively. The two stages are usually designed independently, but we show that this may be suboptimal due to interaction between the stages. In this paper we consider the two stages holistically, as components of a multiple classifier system. This allows for an optimal design that accounts for such interaction. An ROC-based analysis is developed that facilitates the study of the inter-stage interaction, and an analytic example is then used to compare independently designing each stage to a holistically optimised system, based on cost. The benefit of the proposed analysis is demonstrated practically via a number of experiments. The extension to any number of classes is discussed, highlighting the computational challenges, as well as its application in an imprecise environment. [ABSTRACT FROM AUTHOR]

Unlike fixed combining rules, the trainable combiner is applicable to ensembles of diverse base classifier architectures with incomparable outputs. The trainable combiner, however, requires the additional step of deriving a second-stage training dataset from the base classifier outputs. Although several strategies have been devised, it is thus far unclear which is superior for a given situation. In this paper we investigate three principal training techniques, namely the re-use of the training dataset for both stages, an independent validation set, and the stacked generalization. On experiments with several datasets we have observed that the stacked generalization outperforms the other techniques in most situations, with the exception of very small sample sizes, in which the re-using strategy behaves better. We illustrate that the stacked generalization introduces additional noise to the second-stage training dataset, and should therefore be bundled with simple combiners that are insensitive to the noise. We propose an extension of the stacked generalization approach which significantly improves the combiner robustness. [ABSTRACT FROM AUTHOR]