Abstract
The family of instance-based learning algorithms have been shown to be effective for learning classification schemes in many domains. However, it demands high data retention rate and is sensitive to noise. We investigate an integration of instance-filtering and instance-averaging techniques to solve the problem. We compare different variants of integration as well as existing learning algorithms such as C4.5 and KNN. Our new framework shows good performance in data reduction while maintaining or even improving classification accuracy in 19 real data sets.
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Lam, W., Keung, CK., Ling, C.X. (2001). Learning via Prototype Generation and Filtering. 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_13
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DOI: https://doi.org/10.1007/978-1-4757-3359-4_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4861-8
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