Correlated online k-nearest neighbors regressor chain for online multi-output regression
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 c...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2024
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/45047/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Malaya |
| Summary: | 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. |
|---|