A methodology for automated feature engineering in training of shallow neural networks for prediction of multi-linear growth failures (“Nova Algorithm”)

Deep learning requires a large data set. When working with smaller data sets we need an automated approach for feature learning as a pre-processing step to create the training set for a shallow neural network with one hidden layer. The outcome of the neural net classifier is filtered using post-processing rules over a defined wait and quiet period to create an ensemble prediction with higher quality. This gives us the benefits of deep learning, but with a smaller data set. A partial implementation of this methodology was used for a pilot release for prediction of Common Previsioning Group (CPG) growth failures in 3PAR storage devices, using a trained neural net model with additional derived attributes, pre-processing rules and post-processing steps, applied to 90 days history of logical disk free space for each CPG on the storage device