A Theoretical Study of the Representational Power of Weighted Randomised Univariate Regression Tree Ensembles

Amir Ahmad, Sami M. Halawani, Ajay Kumar, Arshad Hashmi, Mutasem Jarrah, Abdul Rafey Ahmad and Zia Abbas
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Amir Ahmad: College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
Sami M. Halawani: ��IT Department, Faculty of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Ajay Kumar: ��Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India
Arshad Hashmi: �Department of Information Systems, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
Mutasem Jarrah: ��IT Department, Faculty of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Abdul Rafey Ahmad: �Department of Computer Science and Engineering-Cyber Security, Indian Institute of Information Technology, Kottayam, India
Zia Abbas: ��Center for VLSI and Embedded Systems Technology (CVEST), International Institute of Information Technology, Hyderabad (IIIT-H), Gachibowli, Hyderabad 500032, India

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 04, 1-26

Abstract: Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees.

Keywords: Ensembles; decision trees; representational power; regression; classification; weights (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S021964922450045X

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