Generalized cascade classification model with customized transformation based ensembles

Classification of high-dimensional data with imbalanced classes poses problems. Especially such time series classification tasks are problematic, because the ordering of each time step (feature) is important and therefore dimensionality reduction and feature selection cannot be applied. The cascade classification model was developed for such time series classification tasks. The cascade classifier splits high-dimensional classification tasks into a cascade of low-dimensional tasks. But the cascade classification model can only handle data sets with a data structure that can be easily learned in low-dimensional space. In this paper, we propose a generalized version of the cascade classification model that can also deal with data sets with more complex data structures. Generalization is achieved with time series transformations and an ensemble of classifiers based on the time series classifier: transformation based ensembles. For this purpose the cascade classifier is integrated into transformation based ensembles with some adjustments. In a simulation study we apply the generalized cascade classification model to predict the realizability (feasibility) of power production time series for pools of different numbers of micro combined heat and power plants. We show that the choice of the aggregation scheme for the ensemble members in the generalized cascade classification model has a strong impact on the overall classification results. But the choice of a weighting scheme showed hardly any influences on the classification result. Furthermore, data sets of different complexity (different structures in data space) yielded very similar classification results.