Decremental Learning of Evolving Fuzzy Inference Systems: Application to Handwritten Gesture Recognition
- 3.7k Downloads
This paper tackles the problem of incremental and decremental learning of an evolving and customizable fuzzy inference system for classification. We explain the interest of integrating a forgetting capacity in such an evolving system to improve its performances in changing environments. In this paper, we describe two decremental learning strategies to introduce a forgetting capacity in evolving fuzzy inference systems. Both techniques use a sliding window to introduce forgetting in the optimization process of fuzzy rules conclusions. The first approach is based on a downdating technique of least squares solutions for unlearning old data. The second integrates differed directional forgetting in the covariance matrices used in the recursive least square algorithm. These techniques are first evaluated on handwritten gesture recognition tasks in changing environments. They are also evaluated on some well-known classification benchmarks. In particular, it is shown that decremental learning allow to adapt to concept drifts. It is also demonstrated that decremental learning is necessary to maintain the system capacity of learning new classes over time, making decremental learning essential for the life-time use of an evolving and customizable classification system.
KeywordsOnline Classification Handwriting Recognition Incremental Learning Decremental Learning Evolving Fuzzy Inference System Recursive Least Squares Concept Drifts Forgetting
Unable to display preview. Download preview PDF.
- 6.Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
- 7.Gama, J., Sebastião, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338 (2009)Google Scholar
- 8.Hägglund, T.: New estimation techniques for adaptive control (1983)Google Scholar
- 9.Haykin, S.O.: Adaptive Filter Theory, 4th edn. Prentice Hall (2001)Google Scholar
- 12.Lughofer, E.: Evolving fuzzy models: incremental learning, interpretability, and stability issues, applications. VDM Verlag Dr. Müller (2008)Google Scholar
- 13.Renau-Ferrer, N., Li, P., Delaye, A., Anquetil, E.: The ILGDB database of realistic pen-based gestural commands. In: International Conference on Pattern Recognition (ICPR 2012), tsukuba, Japan (November 2012)Google Scholar
- 16.Viard-Gaudin, C., Lallican, P.M., Binter, P., Knerr, S.: The IRESTE On/Off (IRONOFF) dual handwriting database. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition, ICDAR 1999, pp. 455–458 (1999)Google Scholar
- 17.Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)Google Scholar