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Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system.

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Title: Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system.
Authors: Li, Dong1 (AUTHOR) lidong@cczu.edu.cn, Gong, Lanlan1 (AUTHOR), Liu, Shulin2 (AUTHOR), Sun, Xin2 (AUTHOR), Gu, Ming1 (AUTHOR), Qian, Kun1 (AUTHOR)
Source: Journal of Intelligent & Fuzzy Systems. 2022, Vol. 42 Issue 4, p3975-3991. 17p.
Subjects: Learning ability testing, Biological systems, Immune system, Learning ability, Classification algorithms
Abstract: The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Dong%22">Li, Dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lidong@cczu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gong%2C+Lanlan%22">Gong, Lanlan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Shulin%22">Liu, Shulin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Xin%22">Sun, Xin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+Ming%22">Gu, Ming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qian%2C+Kun%22">Qian, Kun</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. 2022, Vol. 42 Issue 4, p3975-3991. 17p.
– Name: Subject
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  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+ability+testing%22">Learning ability testing</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+systems%22">Biological systems</searchLink><br /><searchLink fieldCode="DE" term="%22Immune+system%22">Immune system</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+ability%22">Learning ability</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3233/JIFS-212226
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 3975
    Subjects:
      – SubjectFull: Learning ability testing
        Type: general
      – SubjectFull: Biological systems
        Type: general
      – SubjectFull: Immune system
        Type: general
      – SubjectFull: Learning ability
        Type: general
      – SubjectFull: Classification algorithms
        Type: general
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      – TitleFull: Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system.
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            NameFull: Li, Dong
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            NameFull: Gong, Lanlan
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            NameFull: Liu, Shulin
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            NameFull: Sun, Xin
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            NameFull: Gu, Ming
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            – D: 01
              M: 04
              Text: 2022
              Type: published
              Y: 2022
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              Value: 42
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