Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem

Yann Chevaleyre 1 Jean-Daniel Zucker 1
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In recent work, Dietterich et al. (1997) have presented the problem of supervised multiple-instance learning and how to solve it by building axis-parallel rectangles. This problem is encountered in contexts where an object may have different possible alternative configurations, each of which is described by a vector. This paper introduces the multiple-part problem, which is related to the multiple-instance problem, and shows how it can be solved using the multiple-instance algorithms. These two so-called “multiple“ problems could play a key role both in the development of efficient algorithms for learning the relations between the activity of a structured object and its structural properties and in relational learning. This paper analyzes and tries to clarify multiple-problem solving. It goes on to propose multiple-instance extensions of classical learning algorithms to solve multiple-problems by learning multiple-decision trees (Id3-Mi) and multiple-decision rules (Ripper- Mi). In particular, it suggests a new multiple-instance entropy function and a multiple-instance coverage function. Finally, it successfully applies the multiple-part framework on the well-known mutagenesis prediction problem.
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Conference papers
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Submitted on : Thursday, August 3, 2017 - 5:12:36 PM
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Yann Chevaleyre, Jean-Daniel Zucker. Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem. Canadian Conference on AI 2001, Jun 2001, Ottawa, Canada. pp.204-214, ⟨10.1007/3-540-45153-6_20⟩. ⟨hal-01571852⟩

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