Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques

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.