# ParaFIS:A new online fuzzy inference system based on parallel drift anticipation

## TL;DR

This paper introduces ParaFIS, an online fuzzy inference system with a parallel drift anticipation module designed to better handle abrupt concept drifts in data streams, showing improved reactivity and accuracy.

## Contribution

It presents a novel architecture combining generalized EFS with anticipation to enhance adaptation to brutal concept drifts in data streams.

## Key findings

- Improved reactivity time to concept drifts
- Enhanced accuracy after brutal drifts
- Outperforms similar EFS in experiments

## Abstract

This paper proposes a new architecture of incremen-tal fuzzy inference system (also called Evolving Fuzzy System-EFS). In the context of classifying data stream in non stationary environment, concept drifts problems must be addressed. Several studies have shown that EFS can deal with such environment thanks to their high structural flexibility. These EFS perform well with smooth drift (or incremental drift). The new architecture we propose is focused on improving the processing of brutal changes in the data distribution (often called brutal concept drift). More precisely, a generalized EFS is paired with a module of anticipation to improve the adaptation of new rules after a brutal drift. The proposed architecture is evaluated on three datasets from UCI repository where artificial brutal drifts have been applied. A fit model is also proposed to get a "reactivity time" needed to converge to the steady-state and the score at end. Both characteristics are compared between the same system with and without anticipation and with a similar EFS from state-of-the-art. The experiments demonstrates improvements in both cases.