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A domain adaptation method by incorporating belief function in twin quarter-sphere SVM

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Abstract

Domain adaptation is a representative problem in transfer learning, which aims to tackle the problem of insufficient labeled data in a target domain by exploiting discriminant information from a labeled source domain. Since the source and target domains follow different distributions, source domain data are uncertain with respect to the target domain. Ignoring the uncertainty may lead to unreliable label prediction for the target domain. Despite the many studies that have been done on domain adaptation, most have ignored the adverse impact of uncertain and noisy data on learning an adaptive classifier. Regarding these issues, the present paper introduces a robust to noise domain adaptation method by extending the quarter-sphere SVM classifier. Essentially, the proposed method builds an individual classifier for each available class per domain. Also, a belief theory-based weighting approach is designed to provide noise robustness. The strength of the proposed method is that after constructing and training the source domain classifiers, accessibility to the source domain data is not required, and the existence of only the source domain hyperspheres is sufficient. The effectiveness of the proposed method has been compared to the state-of-the-art methods on 15 tasks taken from two benchmark datasets. The experimental results demonstrate the superiority of the proposed method over state-of-the-art ones in terms of classification accuracy and computational time. Besides, the noise analysis proves the robustness of the proposed method. To prove a meaningful distinction between the evaluation metrics results of the proposed method and the competing ones, the Wilcoxon statistical test has been conducted.

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Notes

  1. http://qwone.com/~jason/20Newsgroups/.

  2. https://cvml.ist.ac.at/AwA/.

  3. http://ai.bu.edu/M3SDA/#dataset.

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Authors and Affiliations

  1. Faculty of Computer Engineering and Information Technology, Sadjad University, Mashhad, Iran

    Mona Moradi & Javad Hamidzadeh

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  1. Mona Moradi
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  2. Javad Hamidzadeh
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Contributions

Mona Moradi: Writing - original draft, Resources. Software, Data curation, Writing – review & editing. Javad Hamidzadeh: Conceptualization, Methodology, Validation, Investigation. All authors reviewed the manuscript.

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Correspondence to Javad Hamidzadeh.

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Moradi, M., Hamidzadeh, J. A domain adaptation method by incorporating belief function in twin quarter-sphere SVM. Knowl Inf Syst 65, 3125–3163 (2023). https://doi.org/10.1007/s10115-023-01857-y

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