Improvement of E-MIMLSVM+ Algorithm Based on Semi-Supervised Learning

Wenqing Huang, Hui You (), Li Mei, Yinlong Chen and Mingzhu Huang
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Wenqing Huang: School of Information, Zhejiang Sci-Tech University Hangzhou
Hui You: School of Information, Zhejiang Sci-Tech University Hangzhou
Li Mei: School of Information, Zhejiang Sci-Tech University Hangzhou
Yinlong Chen: School of Information, Zhejiang Sci-Tech University Hangzhou
Mingzhu Huang: School of Information, Zhejiang Sci-Tech University Hangzhou

Chapter Chapter 48 in Recent Developments in Data Science and Business Analytics, 2018, pp 417-423 from Springer

Abstract: Abstract The MIMLSVM algorithm is to transform the MIML learning problem into a single-instance multi-label learning problem, which is used as a bridge to degenerate into a single-instance single-label learning. However, this degradation algorithm is relatively easy to understand, but in the degradation process will lose some information, affecting the classification effect. By using multi-tasking learning, E-MIMLSVM+ is used to combine tag relevance to improve the algorithm MIMLSVM+. In order to make full use of the unlabeled samples to improve the classification accuracy, the paper improves MIMLSVM algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy.

Keywords: SVM; MIML; Semi-supervised learning; Multitask learning (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-72745-5_48

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