# Multi-Source Transfer Learning for Non-Stationary Environments

## TL;DR

This paper introduces Melanie, a novel multi-source transfer learning approach for data stream mining that effectively handles concept drift by leveraging multiple sources to improve predictive accuracy in non-stationary environments.

## Contribution

Melanie is the first method to transfer knowledge across multiple data streaming sources in non-stationary environments, enhancing adaptation to concept drift.

## Key findings

- Melanie outperforms existing algorithms on synthetic data streams with various drifts.
- It effectively leverages multiple sources to improve prediction accuracy.
- Demonstrates robustness across real-world data streams.

## Abstract

In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie). Melanie is the first approach able to transfer knowledge between multiple data streaming sources in non-stationary environments. It creates several sub-classifiers to learn different aspects from different source and target concepts over time. The sub-classifiers that match the current target concept well are identified, and used to compose an ensemble for predicting examples from the target concept. We evaluate Melanie on several synthetic data streams containing different types of concept drift and on real world data streams. The results indicate that Melanie can deal with a variety drifts and improve predictive performance over existing data stream learning algorithms by making use of multiple sources.