Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
33rd (2019)
Session ID : 4A3-J-1-05
Conference information

An extension method of semi-supervised boosting to multiclass classification
*Yuta SAKAIKazuki YASUIKenta MIKAWAMasayuki GOTO
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CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

Recent years, semi-supervised learning which classifies test data into correct category using not only training (labeled) data but a large number of unlabeled data has paid attention. However, in the semi-supervised learning setting, there is a problem that classification accuracy deteriorates because distribution of labeled data is biased. The SemiBoost is one of semi-supervised learning method to solve the problem. The SemiBoost is a binary classification method. However, this method can not be extended directly to multi-class classification. In this research, we propose the way to extend the SemiBoost for multi-class classification using the concept of Error Correcting Output Code (ECOC) method. To verify the effectiveness of our proposed method, we conduct simulation experiment using UCI machine learning repository.

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© 2019 The Japanese Society for Artificial Intelligence
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