Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

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.

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“…Therefore, it can be concluded from Table 20 that the proposed method outperformed the other methods. While the differences between outputs are small for some methods [18,25,34], the improvement provided by the proposed method over some state-of-the-art methods [26][27][28][29][30][31]37,41,43,46,54] is rather significant.…”

“…In order to show the superiority of our method, we compared it with the state-ofthe-art methods in the literature [18,. Some of them are tree-based methods that build the model in the tree architecture by splitting the dataset into various subsets, consisting of decision nodes and leaf nodes, such as the mathematical programming tree (MPtree) [50], model tree regression (M5P) [44], conditional inference tree (CTree) [33], evolutionary tree (Evtree) [50], StatTree [33], classification and regression tree (CART) [34], and reduced error pruning tree (REPTree) [44]. Some of the methods were combined with an optimization algorithm to build one optimal model for predicting the target, such as particle swarm optimization (PSO) [28], optics-inspired optimization (OIO) [26], teachinglearning-based optimization (TLBO) [30], whale optimization algorithm (WAO) [41], ant colony optimization (ACO) [12], and Harris hawks optimization (HHO) [43].…”

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