Wu, Zipeng and Loo, Chu Kiong and Pasupa, Kitsuchart (2024) Correlated online k-nearest neighbors regressor chain for online multi-output regression. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14449. 28 – 39. ISSN 0302-9743, DOI https://doi.org/10.1007/978-981-99-8067-3_3.
Full text not available from this repository.Abstract
Online multi-output regression is a crucial task in machine learning with applications in various domains such as environmental monitoring, energy efficiency prediction, and water quality prediction. This paper introduces CONNRC, a novel algorithm designed to address online multi-output regression challenges and provide accurate real-time predictions. CONNRC builds upon the k-nearest neighbor algorithm in an online manner and incorporates a relevant chain structure to effectively capture and utilize correlations among structured multi-outputs. The main contribution of this work lies in the potential of CONNRC to enhance the accuracy and efficiency of real-time predictions across diverse application domains. Through a comprehensive experimental evaluation on six real-world datasets, CONNRC is compared against five existing online regression algorithms. The consistent results highlight that CONNRC consistently outperforms the other algorithms in terms of average Mean Absolute Error, demonstrating its superior accuracy in multi-output regression tasks. However, the time performance of CONNRC requires further improvement, indicating an area for future research and optimization. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | E-learning; Energy efficiency; Forecasting; Learning systems; Motion compensation; Nearest neighbor search; Regression analysis; Water quality; Efficiency predictions; Environmental Monitoring; Machine-learning; Multi output regression; Multi-output; Novel algorithm; Online machine learning; Online machines; Real-time prediction; Water quality predictions; Machine learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Mar 2024 04:28 |
| Last Modified: | 14 Mar 2024 04:28 |
| URI: | http://eprints.um.edu.my/id/eprint/45047 |
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