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Learning label-specific features for decomposition-based multi-class classification |
Bin-Bin JIA1, Jun-Ying LIU1, Jun-Yi HANG2,3, Min-Ling ZHANG2,3( ) |
1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3. Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing 210096, China |
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Abstract Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules, e.g., one-vs-one, one-vs-rest, error-correcting output codes. Existing works solve these binary classification problems in the original feature space, while it might be suboptimal as different binary classification problems correspond to different positive and negative examples. In this paper, we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative examples. Specifically, to generate the label-specific features, clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster centers. Experiments clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.
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| Keywords
machine learning
multi-class classification
error-correcting output codes
label-specific features
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Corresponding Author(s):
Min-Ling ZHANG
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| About author: * These authors contributed equally to this work. |
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Just Accepted Date: 14 June 2023
Issue Date: 04 July 2023
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