Extracting and reusing blocks of knowledge in learning classifier systems for text classification: a lifelong machine learning approach
- 118 Downloads
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.
KeywordsLearning 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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Arif MH, Jin X, Li J, Iqbal M (2017a) Text classification using lifelong machine learning. In: Proceedings of the 24th international conference on neural information processing (ICONIP), Springer International Publishing, pp 394–404Google Scholar
- Arif MH, Li J, Iqbal M (2017b) Solving social media text classification problems using code fragment based XCSR. In: Proceedings of the international conference on tools with artificial intelligence (ICTAI), Boston, MA, USA, pp 485–492Google Scholar
- Arif MH, Li J, Iqbal M, Peng H (2017c) Optimizing XCSR for text classification. In: Proceedings of the IEEE symposium on service-oriented system engineering (SOSE), San Francisco, CA, USA, pp 86–95Google Scholar
- Chen Z, Ma N, Liu B (2015) Lifelong learning for sentiment classification. In: Proceedings of 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2015), vol 2. Association for Computational Linguistics (ACL), pp 750–756Google Scholar
- Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, Portland, Oregon, USA, pp 142–150Google Scholar
- Shu L, Xu H, Liu B (2017) Lifelong learning CRF for supervised aspect extraction. In: Accepted at ACL 2017, Vancouver, Canada. Association for Computational LinguisticsGoogle Scholar
- Silver DL, Yang Q, Li L (2013) Lifelong machine learning systems: beyond learning algorithms. In: AAAI spring symposium series, California, USA, pp 49–55Google Scholar
- Sutton RS, Koop A, Silver D (2007) On the role of tracking in stationary environments. In: In Proceedings of the 24th international conference on machine learning, (ICML ’07), New York, NY, USA. ACM, pp 871–878Google Scholar
- Urbanowicz RJ, Moore JH (2009) Learning classifier systems: a complete introduction, review, and roadmap. J Artif Evol Appl 2009:1–25Google Scholar