The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification Challenge

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, AUC_ROC = 0.99 and accuracy of 95.6% for Ford_A and Max_KS2 = 0.88, AUC_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.