TPOT-MTR: A Multiple Target Regression Based on Genetic Algorithm of Automated Machine Learning Systems

Authors

  • Hanafi Majid Malaysia Board of Technologists (MBOT), 62000 Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia
  • Syahid Anuar Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia
  • Noor Hafizah Hassan Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.37934/araset.30.3.104126

Keywords:

Multi-target regression, automated machine learning, genetic algorithm, regression analysis, multi-output regression

Abstract

The concept that a cross correlation might improve prediction error underpins machine learning algorithms for multi-target regression (MTR). Numerous MTR approaches have been created in recent years, however there are still uncertainties concerning how their performances are impacted by dataset properties such as linearity, number of targets, and cross correlational complexity. In order to contribute to a better understanding of the relationship between dataset properties and MTR methods, authors proposed a new model of TPOT-MTR, which its result will be compared to previously generated 33 synthetic datasets with controlled characteristics and tested their performance against other two MTR methods, Random Forest and SVM. The results demonstrated that TPOT-MTR approaches could enhance performance even in datasets with non-linearly correlated targets, although the prediction improvement varies depending on the method and regressor combinations used.

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Author Biographies

Hanafi Majid, Malaysia Board of Technologists (MBOT), 62000 Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia

hanafimajid@graduate.utm.my

 

Syahid Anuar, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

syahid.anuar@utm.my

 

Noor Hafizah Hassan , Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

noorhafizah.kl@utm.my

Published

2023-05-15

Issue

Section

Articles