A Probabilistic Approach for Detecting Real Concept Drift

Sirvan Parasteh, Samira Sadaoui

2024

Abstract

Concept Drift (CD) is a significant challenge in real-world data stream applications, as its presence requires predictive models to adapt to data-distribution changes over time. Our paper introduces a new algorithm, Probabilistic Real-Drift Detection (PRDD), designed to track and respond to CD based on its probabilistic definitions. PRDD utilizes the classifier’s prediction errors and confidence levels to detect specifically the Real CD. In an exhaustive empirical study involving 16 synthetic datasets with Abrupt and Gradual drifts, PRDD is compared to well-known CD detection methods. PRDD is highly performing and shows a time complexity of O(1) per datapoint, ensuring its computational efficiency in high-velocity environments.

Paper Citation

in Harvard Style

Parasteh S. and Sadaoui S. (2024). A Probabilistic Approach for Detecting Real Concept Drift. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 301-311. DOI: 10.5220/0012378800003636

in Bibtex Style

@conference{icaart24,
author={Sirvan Parasteh and Samira Sadaoui},
title={A Probabilistic Approach for Detecting Real Concept Drift},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={301-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012378800003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - A Probabilistic Approach for Detecting Real Concept Drift
SN - 978-989-758-680-4
AU - Parasteh S.
AU - Sadaoui S.
PY - 2024
SP - 301
EP - 311
DO - 10.5220/0012378800003636
PB - SciTePress