Reactive Soft Prototype Computing for Concept Drift Streams

TL;DR

This paper introduces a reactive soft prototype computing approach for concept drift streams, enabling stable and rapid adaptation of models in non-static data environments like social media.

Contribution

It proposes a novel concept drift detection and prototype-based adaptation strategy that improves stability and speed during model updates.

Findings

Provides stable performance during concept drift

Enables quick adaptation to changing data

Validated on various non-static datasets

Abstract

The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade. This is because the use of these systems, e. g. social media, has become commonplace in today's society. This requires analytical algorithms to learn and predict this stream of information in real-time. The nature of these systems is non-static and can be explained, among other things, by the fast pace of trends. This creates an environment in which algorithms must recognize changes and adapt. Recent work shows vital research in the field, but mainly lack stable performance during model adaptation. In this work, a concept drift detection strategy followed by a prototype-based adaptation strategy is proposed. Validated through experimental results on a variety of typical non-static data, our solution provides stable and quick adjustments in times of change.