Prediction measurement with mean acceptable error for proper inconsistency in noisy weldability prediction data

• Published in: Robotics and computer-integrated manufacturing 2017-02, Vol.43, p.18-29
• Main Authors: Kim, Kyoung-Yun, Park, Junheung, Sohmshetty, Raj
• Format: Article
• Language: English
• Subjects: Data inconsistency; Mean acceptable error; Prediction measurement; Proper inconsistency; Weldability prediction
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2016.01.002

Due to the complex nature of the welding process, the data used to construct prediction models often contain a significant amount of inconsistency. In general, this type of inconsistent data is treated as noise in the literature. However, for the weldability prediction, the inconsistency, which we describe as proper-inconsistency, may not be eliminated since the inconsistent data can help extract additional information about the process. This paper discusses that, in the presence of proper-inconsistency, it is inappropriate to perform the same approach generally employed with machine learning algorithms, in terms of the model construction and prediction measurement. Due to the numerical characteristics of proper-inconsistency, it is likely to achieve vague prediction results from the prediction model with the traditional prediction performance measures. In this paper, we propose a new prediction performance measure called mean acceptable error (MACE), which measures the performance of prediction models constructed with the presence of proper-inconsistency. This paper presents experimental results with real weldability prediction data, and we examine the prediction performance of k-nearest neighbor (kNN) and generalized regression neural network (GRNN) measured by MACE and the different characteristics of data in relation to MACE, kNN, and GRNN. The results indicate that using a smaller k on properly-inconsistent data increases the prediction performance measured by MACE. Also, the prediction performance on the correct data increases, while the effect of properly-inconsistent data decreases with the measurement of MACE. [Display omitted] •Mean acceptable error (MACE) measures noisy weldability data for prediction accuracy.•The effect of noisy data on prediction accuracy is investigated using MACE.•k nearest neighbor regression and generalized regression neural network are compared.•Three different acceptance levels for MACE allow the level of acceptable prediction.