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Partial Covers and Inhibitory Decision Rules with Weights

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Part of the Studies in Computational Intelligence book series (SCI,volume 163)

Abstract

In this chapter, we consider the case, where each subset, used for covering, has its own weight, and we should minimize the total weight of subsets in partial cover. The same situation is with partial inhibitory decision rules: each conditional attribute has its own weight, and we should minimize the total weight of attributes occurring in partial inhibitory decision rule. If weights of attributes characterize time complexity of attribute value computation, then we try to minimize total time complexity of computation of attributes from partial inhibitory decision rule. If weights characterize a risk of attribute value computation (as in medical or technical diagnosis), then we try to minimize total risk, etc.

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© 2009 Springer-Verlag Berlin Heidelberg

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Delimata, P., Moshkov, M.J., Skowron, A., Suraj, Z. (2009). Partial Covers and Inhibitory Decision Rules with Weights. In: Inhibitory Rules in Data Analysis. Studies in Computational Intelligence, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85638-2_5

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  • DOI: https://doi.org/10.1007/978-3-540-85638-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85637-5

  • Online ISBN: 978-3-540-85638-2

  • eBook Packages: EngineeringEngineering (R0)

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