An Optimal Temporal and Feature Space Allocation in Supervised Data Mining

This paper presents an expository study of temporal data mining for prediction of a future response sequence via mining large number of highly correlated concurrent time series. In the study, we investigate a two dimensional search scheme over time domain weighting and feature space selection. The weighting of observation records over time domain is used to exploit the time dependency structure and feature space selection is enforced to avoid the over-fitting issue. For a specific temporal and spatial selection, its area under ROC curve (AUC) is used to evaluate the prediction performance over the training and testing data. By varying the weighting scheme and feature selection, AUC contour maps on both training and testing data are generated. The contour maps can suggest us to apply the optimal allocations with highest AUC for future responses prediction in training, testing , and possible validation data. Numerical results over two sets of temporal data with different applications have shown that the proposed scheme can improve the prediction performance of conventional data mining methods significantly.