Drift anticipation with forgetting to improve evolving fuzzy system

TL;DR

This paper introduces a novel method for integrating forgetting into Evolving Fuzzy Systems by using concept drift anticipation, enhancing adaptability to non-stationary data streams while maintaining system stability.

Contribution

It proposes a coherent approach to incorporate forgetting in EFS through anticipation, addressing the stability-plasticity dilemma in evolving data streams.

Findings

Improved adaptation to concept drifts in data streams.

Outperforms state-of-the-art online classifiers on benchmark datasets.

Maintains coherence between premise and conclusion parts of EFS.

Abstract

Working with a non-stationary stream of data requires for the analysis system to evolve its model (the parameters as well as the structure) over time. In particular, concept drifts can occur, which makes it necessary to forget knowledge that has become obsolete. However, the forgetting is subjected to the stability-plasticity dilemma, that is, increasing forgetting improve reactivity of adapting to the new data while reducing the robustness of the system. Based on a set of inference rules, Evolving Fuzzy Systems-EFS-have proven to be effective in solving the data stream learning problem. However tackling the stability-plasticity dilemma is still an open question. This paper proposes a coherent method to integrate forgetting in Evolving Fuzzy System, based on the recently introduced notion of concept drift anticipation. The forgetting is applied with two methods: an exponential forgetting of the premise part and a deferred directional forgetting of the conclusion part of EFS to preserve the coherence between both parts. The originality of the approach consists in applying the forgetting only in the anticipation module and in keeping the EFS (called principal system) learned without any forgetting. Then, when a drift is detected in the stream, a selection mechanism is proposed to replace the obsolete parameters of the principal system with more suitable parameters of the anticipation module. An evaluation of the proposed methods is carried out on benchmark online datasets, with a comparison with state-of-the-art online classifiers (Learn++.NSE, PENsemble, pclass) as well as with the original system using different forgetting strategies.