NDLI: Meta-learning for Selecting a Multi-label Classification Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Chekina, L. Rokach, L. Shapira, B.
Copyright Year	2011
Abstract	Although various algorithms for multi-label classification have been developed in recent years, there is little, if any, information as to when each method is beneficial. The main goal of this paper is to compare the classification performance of several multi-label algorithms and to develop a set of rules or tools that will help in selecting the optimal algorithm according to a specific dataset and target evaluation measure. We utilize a meta-learning approach allowing fast automatic selection of the most appropriate algorithm for an unseen dataset based on its descriptive characteristics. We also define a list of characteristics specific for multi-label datasets. The experimental results indicate the applicability and usefulness of the meta-learning approach.
Starting Page	220
Ending Page	227
File Size	296330
Page Count	8
File Format	PDF
ISBN	9781467300056
DOI	10.1109/ICDMW.2011.118
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy dataset characteristics Measurement uncertainty Meta-learning Prediction algorithms Loss measurement Entropy Classification algorithms multi-label classification evaluation measures
Content Type	Text
Resource Type	Article

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Meta-learning for Selecting a Multi-label Classification Algorithm