Classification of Ford Motor Data

In this work we apply mixed ensemble models in order to build a classifier for the Ford Classification Challenge. We build feature vectors from the data sequences in terms of first order statistics, spectral density and autocorrelation. Our model selection scheme is a mixture of cross-validation and bagging. The outcome is an ensemble model, that consits of several different models trained on random subsamples of the entire data set.