Randomized K-d tree ReliefF algorithm for feature selection in handling high dimensional process parameter data
Sitong Xu, Xiang Li, Wen Feng Lu · 2016
In complex manufacturing processes, large amounts of process parameters are monitored and recorded, creating a high-dimensional and heterogonous data warehouse. In order to improve process yield and ensure product quality, comprehensive knowledge of the process should be acquired and critical features should be identified. However, in modern industry production, big data has become quite common; online monitoring and prediction is also usually required. Therefore, an effective feature selection algorithm for industry applications should be fast and robust. Traditional feature selection methods often fails to deal with such demands very well. In this paper, a modified approach for feature selection based on ReliefF is proposed for modelling and analysis of complex manufacturing processes, with improved speed and stability. Randomized k-d tree search is introduced to speed up the feature selection algorithm. The proposed method is also tested with two datasets from real industry process.