Soft Computing

, Volume 23, Issue 23, pp 12673–12682 | Cite as

Extracting and reusing blocks of knowledge in learning classifier systems for text classification: a lifelong machine learning approach

  • Muhammad Hassan ArifEmail author
  • Muhammad Iqbal
  • Jianxin Li
Methodologies and Application


Human beings follow a continuous learning paradigm, i.e., they learn to solve smaller and relatively easy problems, retain the learnt knowledge and apply that knowledge to learn and solve more complex and large-scale problems of the domain. Currently, most machine learning and evolutionary computing systems lack this ability to reuse the previous learnt knowledge. This paper presents a lifelong machine learning model for text classification that extracts the useful knowledge from simple problems of a domain and reuses the learnt knowledge to learn complex problems of the domain. The proposed approach adopts a rule-based learning classifier system, and a rich encoding scheme is used to extract and reuse building units of knowledge. The experimental results show that the continuous learning approach outperformed the baseline classifier system.


Learning classifier systems Lifelong learning Code fragments Transfer learning 



This work is supported by NSFC program (Nos. 61472022, 61421003), SKLSDE-2016ZX-11 and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing.

Compliance with ethical standard

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Advanced Innovation Center for Big Data and Brain Computing, School of Computer Science and EngineeringBeihang University (BUAA)BeijingChina
  2. 2.Faculty of Computer Information ScienceHigher Colleges of TechnologyFujairahUnited Arab Emirates

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