Authors:
Imen Trabelsi
1
;
2
;
Besma Zeddini
3
;
Marc Zolghadri
4
;
2
;
Maher Barkallah
1
and
Mohamed Haddar
1
Affiliations:
1
LA2MP Laboratory, ENIS, Route Soukra Km 3.5, 3038 Sfax, Tunisia
;
2
Quartz Laboratory, SUPMECA, 3 rue Fernand Hainaut, 93407 Saint-Ouen, France
;
3
SATIE Laboratory CNRS, UMR 8029, CYTeh ENS Paris-Saclay, Cergy, France
;
4
LAAS - CNRS, 7 Avenue du Colonel Roche, 31400 Toulouse, France
Keyword(s):
Obsolescence Prediction, Artificial Intelligence, Machine Learning, Feature Selection.
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.