Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window

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.