Skip to Main content Skip to Navigation
Conference papers

Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window

Manuel Bouillon 1, * Eric Anquetil 1 Abdullah Almaksour 1
* Corresponding author
1 IntuiDoc - intuitive user interaction for document
Abstract : This paper tackles the problem of decremental learning of an evolving classification system. We study the use of decremental learning to improve performance of evolving recognizers in non-stationary scenarios. Our on-line recognizer is based on an evolving fuzzy inference system. In this paper, we propose a new strategy to introduce decremental learning, with the use of a sliding window, in the optimization of fuzzy rules conclusions. This approach is based on a downdating technique of least squares solutions for unlearning old data. This technique is evaluated on handwritten gesture recognition tasks. In particular, it is shown that this downdating techniques allow to adapt to concept drifts and that we face a precision reactiveness trade-off. It is also demonstrated that decremental learning is necessary to maintain the system learning capacity over time, making decremental learning essential for the life-time use of an evolving classification system.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download
Contributor : Manuel Bouillon Connect in order to contact the contributor
Submitted on : Tuesday, October 16, 2012 - 4:01:41 PM
Last modification on : Tuesday, October 19, 2021 - 11:58:55 PM
Long-term archiving on: : Thursday, January 17, 2013 - 7:55:17 AM


Files produced by the author(s)



Manuel Bouillon, Eric Anquetil, Abdullah Almaksour. Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window. Eleventh International Conference on Machine Learning and Applications (ICMLA), Dec 2012, Boca Raton, United States. pp.598-601, ⟨10.1109/ICMLA.2012.110⟩. ⟨hal-00742570⟩



Les métriques sont temporairement indisponibles