Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

Jui-Sheng Chou, Dinh-Nhat Truong and Chih-Fong Tsai
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Jui-Sheng Chou: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Dinh-Nhat Truong: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Chih-Fong Tsai: Department of Information Management, National Central University, Taoyuan City 320317, Taiwan

Mathematics, 2021, vol. 9, issue 6, 1-25

Abstract: Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

Keywords: applied machine learning; classification and regression; data mining; ensemble model; engineering informatics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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