Abstract
In this chapter, we consider instance selection as a focusing task in the data preparation phase of knowledge discovery and data mining. Focusing covers all issues related to data reduction. First, we define a broader perspective on focusing tasks, choose instance selection as one particular focusing task, and outline the specification of evaluation criteria to measure success of instance selection approaches. Thereafter, we present a unifying framework that covers existing approaches for instance selection as instantiations. We describe a specific example instantiation of this framework and discuss its strengths and weaknesses. Then, we propose an enhanced framework for instance selection, generic sampling, and summarize evaluation results for several instantiations of its implementation. Finally, we conclude with open issues and research challenges for instance selection as well as focusing in general.
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Reinartz, T. (2001). A Unifying View on Instance Selection. In: Liu, H., Motoda, H. (eds) Instance Selection and Construction for Data Mining. The Springer International Series in Engineering and Computer Science, vol 608. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3359-4_3
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DOI: https://doi.org/10.1007/978-1-4757-3359-4_3
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