Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescenceopen access

Authors
Moon, Kyoung-SookLee, Hee WonKim, Hongjoong
Issue Date
10월-2022
Publisher
MDPI
Keywords
component obsolescence; diminishing manufacturing sources and material shortages; forecasting; machine learning; unsupervised clustering
Citation
SENSORS, v.22, no.20
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
22
Number
20
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/146583
DOI
10.3390/s22207982
ISSN
1424-8220
Abstract
Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Science > Department of Mathematics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher KIM, HONG JOONG photo

KIM, HONG JOONG
이과대학 (수학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE