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Hierarchical Model for Rank Discrimination Measures

Christophe Marsala 1 Davide Petturiti
1 LFI - Learning, Fuzzy and Intelligent systems
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In this paper we focus on rank discrimination measures, i.e., functions able to quantify the discrimination power of an attribute w.r.t. the class, taking into account the monotonicity of the class w.r.t. the attribute. These measures are used in decision tree induction in order to enforce a local form of monotonicity of the class w.r.t. the splitting attribute and are characterized by a noticeable robustness to non-monotone noise present in the data. More precisely, here we present a hierarchical model in order to single out which properties a function must satisfy to be a rank discrimination measure, providing in this way a framework for the construction of new measures.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01219707
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Submitted on : Friday, October 23, 2015 - 10:21:16 AM
Last modification on : Thursday, March 21, 2019 - 1:07:09 PM

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Christophe Marsala, Davide Petturiti. Hierarchical Model for Rank Discrimination Measures. 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2013, Jul 2013, Utrecht, Netherlands. pp.412-423, ⟨10.1007/978-3-642-39091-3_35⟩. ⟨hal-01219707⟩

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