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A classification learning algorithm robust to irrelevant features

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1480))

Abstract

Presence of irrelevant features is a fact of life in many real-world applications of classification learning. Although nearest-neighbor classification algorithms have emerged as a promising approach to machine learning tasks with their high predictive accuracy, they are adversely affected by the presence of such irrelevant features. In this paper, we describe a recently proposed classification algorithm called VFI5, which achieves comparable accuracy to nearest-neighbor classifiers while it is robust with respect to irrelevant features. The paper compares both the nearest-neighbor classifier and the VFI5 algorithms in the presence of irrelevant features on both artificially generated and real-world data sets selected from the UCI repository.

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Fausto Giunchiglia

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© 1998 Springer-Verlag Berlin Heidelberg

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Güvenir, H.A. (1998). A classification learning algorithm robust to irrelevant features. In: Giunchiglia, F. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 1998. Lecture Notes in Computer Science, vol 1480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057452

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  • DOI: https://doi.org/10.1007/BFb0057452

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64993-9

  • Online ISBN: 978-3-540-49793-6

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