Title: A meta-learning system for multi-instance classification
Authors: Vanwinckelen, Gitte ×
Blockeel, Hendrik #
Issue Date: Aug-2014
Host Document: Proceedings of the ECML-14 Workshop on Learning over Multiple Contexts pages:1-14
Conference: International Workshop on Learning over Multiple Contexts edition:1 location:Nancy, France date:19 September 2014
Article number: 17
Abstract: Meta-learning refers to the use of machine learning methods to analyze the behavior of machine learning methods on different types of datasets. Until now, meta-learning has mostly focused on the standard classification setting. In this paper about ongoing work, we apply it to multi-instance classification, an alternative classification setting in which bags of instances, rather than individual instances, are labeled. We define a number of data set properties that are specific to the multi-instance setting, and extend the concept of landmarkers to the multi-instance setting. Experimental results show that multi-instance classifiers are very sensitive to the context in which they are used, and that the meta-learning approach can indeed yield useful insights in this respect.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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