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This article presents an efficient solution for the PAKDD-2007 Competition cross-selling problem. The solution is based on a thorough approach which involves the creation of new input variables, efficient data preparation and transformation, adequate data sampling strategy and a combination of two of the most robust modeling techniques. Due to the complexity imposed by the very small amount of examples in the target class, the approach for model robustness was to produce the median score of the 11 models developed with an adapted version of the 11-fold cross-validation process and the use of a combination of two robust techniques via stacking, the MLP neural network and the n-tuple classifier. Despite the problem complexity, the performance on the prediction data set (unlabeled samples), measured through KS2 and ROC curves was shown to be very effective and finished as the first runner-up solution of the competition.

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This paper presents an approach for solving WCCI 2008's Ford Classification Challenge Problem. The solution is based on the creation of new input variables through temporal feature extraction and on the combination via bagging of an ensemble of 30 multi-layer perceptrons trained on sets divided by multiple random sampling of the labeled data. Signal power, signal to noise ratio and signal frequency were some of the meaningful features extracted for improving the system's performance. The data sampling strategy produced a robust median MLP response and allowed for the definition of the appropriate decision threshold. The performance measured on the 30 test samples (statistically independent from the training data) reached an average of Max_KS2 =0.91, AVC_ROC = 0.99 and accuracy of 95.6 % for Ford_A and Max_KS2 = 0.88, AVC_ROC = 0.98 and accuracy of 94.1 % for Ford_B. These results have been confirmed on the competition for the noiseless data and have degraded around 15% for the noisy data.

This brief generalizes the forecasting method that has been awarded first-place winner in the International Competition of Time Series Forecasting (ICTSF 2012). It is based on a short-term forecasting approach of multilayer perceptrons (MLP) ensembles, combined dynamically with a long-term forecasting. The main feature of this general approach is the original concept of continuous dynamical combination of forecasts, in which the weights of the forecasting combination are a function of forecast horizon. Experiments in ICTSFs and NN5s nonstationary time series show that this new combination method improves the performance in multistep forecasting of MLP ensembles when compared to the MLP ensembles alone.