Abstract
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.
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Acknowledgements
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.
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Arif, M.H., Iqbal, M. & Li, J. Extracting and reusing blocks of knowledge in learning classifier systems for text classification: a lifelong machine learning approach. Soft Comput 23, 12673–12682 (2019). https://doi.org/10.1007/s00500-019-03819-5
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DOI: https://doi.org/10.1007/s00500-019-03819-5