D. Martínez-Galicia, A. Guerra-Hernández, N. Cruz-Ramírez, X. Limón, and F. Grimaldo. Towards Windowing as a Sub-Sampling Method for Distributed Data Mining. Research in Computing Science, 149(3):57–64, 2020.
Abstract. Windowing is a sub-sampling method that enables the induction of decision trees with large datasets. Using a small sample of the available training examples, the method can achieve levels of accuracy comparable or better than those obtained using the full available dataset. More relevant is the fact that Windowing-based strategies for Distributed Data Mining (DDM) have shown a correlation between the accuracy of the learned decision tree and the number of examples used to learn it, i.e., the higher the accuracy, the fewer examples used to induce the model. This paper corroborates that this behavior is also observed when adopting inductive algorithms of a diﬀerent nature than C4.5 or ID3, the algorithms usually adopted when windowing, contributing to the use of Windowing as a general sub-sampling method for DDM. The paper also contributes exploring some metrics to the validation of the obtained sub-samples of examples.