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Co-evolutionary Multi-task Learning for Modular Pattern Classification

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

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Abstract

Modularity in the learning process is a means by which effective decision making can be maintained when some of the input features are missing. In this paper, co-evolutionary multi-task learning algorithm is used for pattern classification which is robust to situations when some input features are unavailable during the deployment stage of decision support or control systems. The main feature of the algorithm is the ability to make decisions with some degree of error given misinformation. The results show that the method produces results comparable to non-modular methods while having modular features for dynamic and robust pattern classification.

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Notes

  1. 1.

    https://github.com/rohitash-chandra/CMTL-patternclassification.

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Author information

Authors and Affiliations

  1. Centre for Translational Data Science, The University of Sydney, Sydney, NSW, 2006, Australia

    Rohitash Chandra

Authors
  1. Rohitash Chandra
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Corresponding author

Correspondence to Rohitash Chandra .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Chandra, R. (2017). Co-evolutionary Multi-task Learning for Modular Pattern Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_73

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  • DOI: https://doi.org/10.1007/978-3-319-70136-3_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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