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Sequential semi-supervised active learning model in extremely low training set (SSSAL)

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Abstract

With the rapid development of computing and multimedia technology, the volume of web traffic data, social networks, sensors and other types of resources is increasing rapidly. Most of the data are unlabeled, and only a very small percentage of it is labeled. In some cases, data tags can be obtained free or at a low cost, while for many complex tasks such as speech recognition, data extraction, classification and filtering, tagging is usually difficult, time-consuming and expensive, because it requires manual interpretation by human experts. Active learning examines the issue by selecting the most active data to query tags and achieving high learning accuracy with a small number of tagged items. There are different approaches for data classification which have disadvantages such as low accuracy or high computational time. In this study, by combining covariance-based self-expression correlation estimation and incremental active learning methods, a new three-step method to sequential semi-supervising classification is presented. Initially, a new method based on covariance is used to estimate the correlation between samples. Then, a semi-supervised active learning algorithm based on GAN (Generative Adversarial Networks) is introduced to perform the learning process. Finally, a new incremental learning model based on support vector machine is used for classification. We use 20-Newsgroups, Web KB and Image Net datasets to test the performance of our proposed method. The experimental results show the robustness and effectiveness of the proposed method in terms of accuracy, precision, recall and f-measure factors compared to other methods.

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Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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All code for data analysis associated with the current submission is available from the corresponding author upon reasonable request.

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Funding

This paper is extracted from the PHd thesis and has no funding from any organizations.

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Authors and Affiliations

  1. Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Ebrahim Khalili, Razieh Malekhosseini, S. Hadi Yaghoubyan & Karamollah Bagherifard

  2. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

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  1. Ebrahim Khalili
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  3. S. Hadi Yaghoubyan
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  4. Karamollah Bagherifard
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Contributions

EKAi took part in formal analysis, conceptualization, methodology, data curation, writing—original draft. RM involved in data curation, methodology, validation, formulation, investigation, writing—review & editing, supervision. SHY involved in formal analysis, data curation, methodology, conceptualization, writing—review & editing. KB took part in formal analysis, conceptualization, writing—review & editing. HP took part in conceptualization, formulation, writing—review & editing.

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Correspondence to Razieh Malekhosseini.

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Khalili, E., Malekhosseini, R., Yaghoubyan, S.H. et al. Sequential semi-supervised active learning model in extremely low training set (SSSAL). J Supercomput 79, 6646–6673 (2023). https://doi.org/10.1007/s11227-022-04847-z

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