Solving regression problems with intelligent machine learner for engineering informatics

Jui Sheng Chou, Dinh Nhat Truong, Chih Fong Tsai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Article number686
JournalMathematics
Volume9
Issue number6
DOIs
StatePublished - 2 Mar 2021

Keywords

  • Applied machine learning
  • Classification and regression
  • Data mining
  • Engineering informatics
  • Ensemble model

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