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Feature Selection Using Consistency Measure

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Discovery Science (DS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1721))

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Abstract

Feature selection methods search for an “optimal” subset of features. Many methods exist. We evaluate consistency measure along with different search techniques applied in the literature and suggest a guideline of its use.

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References

  1. H. Almuallim and T. G. Dietterich. Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1–2):279–305, November 1994.

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  2. M. Dash. Feature selection via set cover. In Proceedings of IEEE Knowledge and Data Engineering Exchange Eorkshop, pages 165–171, Newport, California, November 1997. IEEE Computer Society.

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  3. H. Liu, H. Motoda, and M. Dash. A monotonic measure for optimal feature selection. In Proceedings of European Conference on Machine Learning, pages 101–106, 1998.

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  4. H. Liu and R. Setiono. A probabilistic approach to feature selection-a filter solution. In Proceedings of International Conference on Machine Learning, pages 319–327, 1996.

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© 1999 Springer-Verlag Berlin Heidelberg

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Dash, M., Liu, H., Motoda, H. (1999). Feature Selection Using Consistency Measure. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_30

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  • DOI: https://doi.org/10.1007/3-540-46846-3_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66713-1

  • Online ISBN: 978-3-540-46846-2

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