Hybrid Feature Selection for High-Dimensional Manufacturing Data

In manufacturing environment, hundreds of input parameters are related to product quality. To build an accurate machine learning model for quality prediction, it is necessary to find major input parameters which have a big influence in quality prediction. The procedure of identifying major factors out of original high-dimensional input parameters is called to be feature selection. This paper proposes a hybrid method for feature selection, which effectively reduces the searching space by leveraging feature subset chosen by Fast Correlation Based Filter (FCBF) and Relief-based feature selection. The computational complexity is proved to be quadratic in feature number, while most of the existing methods suffer from exponential computation complexity. This improvement is crucial especially when we deal with high-dimensional input parameters because it dramatically reduces the computational time. Further, the proposed method outperforms in prediction accuracy as well when it compares with the benchmarking method. It has been demonstrated by the implementation of our method into real-world manufacturing data sets and open source benchmarking data set.