Though impressive classification accuracy is often obtained via discrimination-based learning techniques such as Multi-Layer Perceptrons (DMLP), these techniques often assume that the underlying training sets are optimally balanced (in terms of the number of positive and negative examples). Unfortunately, this is not always the case. In this paper, we look at a recognition-based approach whose accuracy in such environments is superior to that obtained via more conventional mechanisms. At the heart of the new technique is a modified auto-encoder that allows for the incorporation of a recognition component into the conventional MLP mechanism. In short, rather than being associated with an output value of "1", positive examples are fully reconstructed at the network output layer while negative examples, rather than being associated with an output value of "0", have their inverse derived at the output layer. The result is an auto-encoder able to recognize positive examples while discriminating against negative ones by virtue of the fact that negative cases generate larger reconstruction errors. A simple technique is employed to exaggerate the impact of training with these negative examples so that reconstruction errors can be more reliably established. Preliminary testing on both seismic and sonar data sets has demonstrated that the new method produces lower error rates than standard connectionist systems in imbalanced settings. Our approach thus suggests a simple and more robust alternative to commonly used classification mechanisms.
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