Abstract
In the previous chapters, various types of splitting criteria were proposed. Each of the presented criteria is constructed using one specific impurity measure (or, more precisely, the corresponding split measure function). Therefore we will refer to such criteria as ‘single’ splitting criteria. The experiments conducted in Chap. 5 demonstrate that various single splitting criteria have their own advantages and drawbacks. Based on this observation a new kind of splitting criteria can be proposed, which combine together two different single criteria in a heuristic manner. We refer to them as hybrid splitting criteria. In this chapter, we discuss premises which demonstrate that such an approach may lead to satisfactory results.
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Rutkowski, L., Jaworski, M., Duda, P. (2020). Hybrid Splitting Criteria. In: Stream Data Mining: Algorithms and Their Probabilistic Properties. Studies in Big Data, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-13962-9_7
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DOI: https://doi.org/10.1007/978-3-030-13962-9_7
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