loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Sabina Gooljar ; Kris Manohar and Patrick Hosein

Affiliation: Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad

Keyword(s): Random Forest, Decision Tree, k-NN, Euclidean Distance, XG Boost, Regression.

Abstract: In recent years, Machine Learning algorithms, in particular supervised learning techniques, have been shown to be very effective in solving regression problems. We compare the performance of a newly proposed regression algorithm against four conventional machine learning algorithms namely, Decision Trees, Random Forest, k-Nearest Neighbours and XG Boost. The proposed algorithm was presented in detail in a previous paper but detailed comparisons were not included. We do an in-depth comparison, using the Mean Absolute Error (MAE) as the performance metric, on a diverse set of datasets to illustrate the great potential and robustness of the proposed approach. The reader is free to replicate our results since we have provided the source code in a GitHub repository while the datasets are publicly available.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.171.164.77

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Gooljar, S.; Manohar, K. and Hosein, P. (2023). Performance Evaluation and Comparison of a New Regression Algorithm. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 524-531. DOI: 10.5220/0012135400003541

@conference{data23,
author={Sabina Gooljar. and Kris Manohar. and Patrick Hosein.},
title={Performance Evaluation and Comparison of a New Regression Algorithm},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={524-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012135400003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - Performance Evaluation and Comparison of a New Regression Algorithm
SN - 978-989-758-664-4
IS - 2184-285X
AU - Gooljar, S.
AU - Manohar, K.
AU - Hosein, P.
PY - 2023
SP - 524
EP - 531
DO - 10.5220/0012135400003541
PB - SciTePress