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Institut de Recherche en Informatique de Toulouse |  |
Journal articles
Learning rule sets and Sugeno integrals for monotonic classification problems
Abstract : In some variants of the supervised classification setting, the domains of the attributes and the set of classes are totally ordered sets. The task of learning a classifier that is nondecreasing w.r.t. each attribute is called monotonic classification. Several kinds of models can be used in this task; in this paper , we focus on decision rules. We propose a method for learning a set of decision rules that optimally fits the training data while favoring short rules over long ones. We give new results on the representation of sets of if-then rules by extensions of Sugeno integrals to distinct attribute domains, where local utility functions are used to map attribute domains to a common totally ordered scale. We study whether such qualitative extensions of Sugeno integral provide compact representations of large sets of decision rules.
Cited literature [59 references]
https://hal.inria.fr/hal-02427608
Contributor : Miguel Couceiro <>
Submitted on : Friday, January 3, 2020 - 5:56:25 PM
Last modification on : Tuesday, January 26, 2021 - 4:14:02 PM
Long-term archiving on: : Monday, April 6, 2020 - 8:21:06 PM
Contributor : Miguel Couceiro <>
Submitted on : Friday, January 3, 2020 - 5:56:25 PM
Last modification on : Tuesday, January 26, 2021 - 4:14:02 PM
Long-term archiving on: : Monday, April 6, 2020 - 8:21:06 PM
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