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Improving Cascade Classifier Precision by Instance Selection and Outlier Generation

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.