Feature extraction and classification models for high‐dimensional profile data

Abstract

As manufacturing transitions to real-time sensing, it becomes more important to handle multiple, high-dimensional (non-stationary) time series that generate thousands of measurements for each batch. Predictive models are often challenged by such high-dimensional data and it is important to reduce the dimensionality for better performance. With thousands of measurements, even wavelet coefficients do not reduce the dimensionality sufficiently. We propose a two-stage method that uses energy statistics from a discrete wavelet transform to identify process variables and appropriate resolutions of wavelet coefficients in an initial (screening) model. Variable importance scores from a modern random forest classifier are exploited in this stage. Coefficients that correspond to the identified variables and resolutions are then selected for a second-stage predictive model. The approach is shown to provide good performance, along with interpretable results, in an example where multiple time series are used to indicate the need for preventive maintenance. In general, the two-stage approach can handle high dimensionality and still provide interpretable features linked to the relevant process variables and wavelet resolutions that can be used for further analysis. he has been a software engineer at Intel Corporation's Arizona site working on statistical process control applications. His interests are in the general areas of statistical model building and both supervised and un-supervised learning methods.Mani Janakiram is a Director/Principal Engineer of Supply Chain Strategy at Intel and in his 11+ years at Intel, he has managed several projects in supply chain, strategy roadmap, modeling, capacity planning, process control, analytics and research. He has 20+ years of experience (including Honeywell and Motorola), published 50+ papers and has two patents. Mani is a Six Sigma Master Black Belt, and served on several committees including, ITRS FI, AZ Tech Council, Stanford AIM, ISMI, NSF research panels and Factory Systems of SRC. He is also an adjunct professor at the Thunderbird School of Global Management. Mani holds a 892

Variable selection wrapper in presence of correlated input variables for random forest models

“…Typically, there are three goals in variable selection. The first is purely technical; dealing with large sets of variables slows down algorithms, consumes excessive resources and is inefficient 1,2 . A second aim is to find a small number of variables that maximize the model accuracy 3 .…”

“…The first is purely technical; dealing with large sets of variables slows down algorithms, consumes excessive resources and is inefficient. 1,2 A second aim is to find a small number of variables that maximize the model accuracy. 3 Numerous machine learning algorithms show a reduction in accuracy when the number of variables is significantly higher than optimal.…”