Abstract
Selection pressures are pervasive. As data grows, the demand for data reduction increases for effective data mining. Instance selection is one of effective means to data reduction. This chapter expounds basic concepts of instance selection, its context, necessity and functionality. It briefly introduces the state-of-the-art methods for instance selection, and presents an overview of the field as well as a summary of contributing chapters in this collection. Its coverage also includes evaluation issues, related work, and future directions.
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Liu, H., Motoda, H. (2001). Data Reduction via 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_1
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DOI: https://doi.org/10.1007/978-1-4757-3359-4_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4861-8
Online ISBN: 978-1-4757-3359-4
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