A Prediction Modeling Framework: Toward Integration of Noisy Manufacturing Data and Product Design

Abstract

In many design and manufacturing applications, data inconsistency or noise is common. These data can be used to create opportunities and/or support critical decisions in many applications, for example, welding quality prediction for material selection and quality monitoring applications. Typical approaches to deal with these data issues are to remove or alter them before constructing any model or conducting any analysis to draw decisions. However, these approaches are limited especially when each data carries important value to extract additional information about the nature of the given problem. In the literature, with the presence of noise in data, bootstrap aggregating has shown an improvement in the prediction accuracy. In order to achieve such an improvement, a bagging model has to be carefully constructed. The base learning algorithm, number of base learning algorithms, and parameters for the base learning algorithms are crucial design parameters in that aspect. Evolutionary algorithms such as genetic algorithm and particle swarm optimization have shown promising results in determining good parameters for different learning algorithms such as multilayer perceptron neural network and support vector regression. However, the computational cost of an evolutionary computation algorithm is usually high as they require a large number of candidate solution evaluations. This requirement even more increases when bagging is involved rather than a single learning algorithm. To reduce such high computational cost, a metamodeling approach is introduced to particle swarm optimization. The meta-modeling approach reduces the number of fitness function evaluations in the particle swarm optimization process and therefore the overall computational cost can be reduced. In this paper, we propose a prediction modeling framework whose aim is to construct a bagging model to improve the prediction accuracy on noisy data. The proposed framework is tested on an artificially generated noisy dataset. The quality of final solutions obtained by the proposed framework is reasonable compared to particle swarm optimization without meta-modeling. In addition, using the proposed framework, the largest improvement in the computational time is about 42 percent.