This paper introduces AURORA, an algorithm that builds binary classifiers to optimise the area under the ROC curve (the AUC). AURORA builds non-linear classifiers of the data by binarising the raw input features. Feature selection is performed using a fast heuristic routine, and gradient descent is used to optimise the coefficients of the selected features. Both feature selection and gradient descent are designed to be optimal for AUC. Non-differentiability of the objective function is overcome using a ramp-based approximation for the AUC. The use of this ramp-based approximation also allows the AUC to be calculated in near O(n) time, where n is the number of labelled observations available. The entire AURORA algorithm then also has computational complexity near O(n). AURORA is compared with several other classifiers, over eight binary classification tasks, and generally produces classifiers which are significantly more accurate. AURORA is also highly scalable with increasing number of training examples, and increasing number of input features.

We demonstrate the practical ability of neural networks (NNs) trained in a supervised mode to extract useful control "knowledge" from a large, high-dimensional empirical database, and then to deliver almost optimal control in "real time". In particular, this paper describes experiments with NN-based controllers for allocating bandwidth capacity in a telecommunications network (SDH). This system was proposed in order to overcome a "real time" response constraint. Two basic architectures, each consisting of a combination of two methods, are evaluated: (1) a feedforward network-heuristic combination and (2) a feedforward network-recurrent network combination. These architectures are compared against a linear programming (LP) optimizer as a benchmark. This LP optimizer was also used as a teacher to label the data samples for the feedforward NN training algorithm. NN-based solutions are very accurate (/spl sim/98% of optimal throughput) and, in contrast to the algorithmic approach, can be delivered in "real time". It is found that while the "human" generated heuristics (greedy search optimization) fail to find a solution in approximately 30% of cases, the best NN fails only in 4.9% of cases. Moreover, it has been found that in spite of the very high dimensionality of the problem (55 inputs and 126 outputs), the solution can be delivered by surprisingly compact NNs, with as little as around 1000 synaptic weights. This proves that on this occasion the NNs were able to extract simple but powerful "heuristics" hidden in the complex sets of numerical data.