Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques

Imen Trabelsi, Imen Trabelsi, Besma Zeddini, Marc Zolghadri, Marc Zolghadri, Maher Barkallah, Mohamed Haddar

2021

Abstract

Obsolescence is a serious phenomenon that affects all systems. To reduce its impacts, a well-structured management method is essential. In the field of obsolescence management, there is a great need for a method to predict the occurrence of obsolescence. This article reviews obsolescence forecasting methodologies and presents an obsolescence prediction methodology based on machine learning. The model developed is based on joint a machine learning (ML) technique and feature selection. A feature selection method is applied to reduce the number of inputs used to train the ML technique. A comparative study of the different methods of feature selection is established in order to find the best in terms of precision. The proposed method is tested by simulation on models of mobile phones. Consequently, the use of features selection method in conjunction with ML algorithm surpasses the use of ML algorithm alone.

Paper Citation

in Harvard Style

Trabelsi I., Zeddini B., Zolghadri M., Barkallah M. and Haddar M. (2021). Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 787-794. DOI: 10.5220/0010241407870794

in Bibtex Style

@conference{icaart21,
author={Imen Trabelsi and Besma Zeddini and Marc Zolghadri and Maher Barkallah and Mohamed Haddar},
title={Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={787-794},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010241407870794},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques
SN - 978-989-758-484-8
AU - Trabelsi I.
AU - Zeddini B.
AU - Zolghadri M.
AU - Barkallah M.
AU - Haddar M.
PY - 2021
SP - 787
EP - 794
DO - 10.5220/0010241407870794