A Hierarchical Anytime k-NN Classifier for Large-Scale High-Speed Data Streams

Aarti, Jagat Challa, Hrishikesh Harsh, Utkarsh D., Mansi Agarwal, Raghav Chaudhary, Navneet Goyal, Poonam Goyal

2024

Abstract

The k-Nearest Neighbor Classifier (k-NN) is a widely used classification technique used in data streams. However, traditional k-NN-based stream classification algorithms can’t handle varying inter-arrival rates of objects in the streams. Anytime algorithms are a class of algorithms that effectively handle data streams that have variable stream speed and trade execution time with the quality of results. In this paper, we introduce a novel anytime k-NN classification method for data streams namely, ANY-k-NN. This method employs a proposed hierarchical structure, the Any-NN-forest, as its classification model. The Any-NN-forest maintains a hierarchy of micro-clusters with different levels of granularity in its trees. This enables ANY-k-NN to effectively handle variable stream speeds and incrementally adapt its classification model using incoming labeled data. Moreover, it can efficiently manage large data streams as the model construction is less expensive. It is also capable of handling concept drift and class evolution. Additionally, this paper also presents ANY-MP-k-NN, a first-of-its-kind framework for anytime k-NN classification of multi-port data streams over distributed memory architectures. ANY-MP-k-NN can efficiently manage very large and high-speed data streams and deliver highly accurate classification results. The experimental findings confirm the superior performance of the proposed methods compared to the state-of-the-art in terms of classification accuracy.

Paper Citation

in Harvard Style

Aarti., Challa J., Harsh H., D. U., Agarwal M., Chaudhary R., Goyal N. and Goyal P. (2024). A Hierarchical Anytime k-NN Classifier for Large-Scale High-Speed Data Streams. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 276-287. DOI: 10.5220/0012367500003636

in Bibtex Style

@conference{icaart24,
author={Aarti and Jagat Challa and Hrishikesh Harsh and Utkarsh D. and Mansi Agarwal and Raghav Chaudhary and Navneet Goyal and Poonam Goyal},
title={A Hierarchical Anytime k-NN Classifier for Large-Scale High-Speed Data Streams},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={276-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012367500003636},
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 Hierarchical Anytime k-NN Classifier for Large-Scale High-Speed Data Streams
SN - 978-989-758-680-4
AU - Aarti.
AU - Challa J.
AU - Harsh H.
AU - D. U.
AU - Agarwal M.
AU - Chaudhary R.
AU - Goyal N.
AU - Goyal P.
PY - 2024
SP - 276
EP - 287
DO - 10.5220/0012367500003636
PB - SciTePress