Improving Cascade Classifier Precision by Instance Selection and Outlier Generation
Judith Neugebauer, Oliver Krämer, Michael Sonnenschein · 2016
Beside the curse of dimensionality and imbalanced classes, unfavorable data distributions can hamper classification accuracy. This is particularly problematic with increasing dimensionality of the classification task. A classifier that can handle high-dimensional and imbalanced data sets is the cascade classification method for time series. The cascade classifier can compound unfavorable data distributions by projecting the high-dimensional data set onto low-dimensional subsets. A classifier is trained for each of the low-dimensional data subsets and their predictions are aggregated to an overall result. For the cascade classifier, the errors of each classifier accumulate in the overall result and therefore small improvements in each small classifier can improve the classification accuracy. Therefore we propose two methods for data preprocessing to improve the cascade classifier. The first method is instance selection, a technique to select representative examples for the classification task. Furthermore, artificial infeasible examples can improve classification performance. Even if high-dimensional infeasible examples are available, their projection to low-dimensional space is not possible due to projection errors. We propose a second data preprocessing method for generating artificial infeasible examples in low-dimensional space. We show for micro Combined Heat and Power plant power production time series and an artificial and complex data set that the proposed data preprocessing methods increase the performance of the cascade classifier by increasing the selectivity of the learned decision boundaries.