Conditional Variable Selection for Intelligent Test

TL;DR

This paper introduces a novel framework for conditional variable selection that enhances intelligent testing by selecting key variables based on preselected ones, addressing scalability and specificity issues in high-dimensional data analysis.

Contribution

It proposes a new conditional variable selection framework tailored for embedded and deep-learning methods, filling a gap in existing variable selection techniques.

Findings

Framework effectively identifies important variables under conditions.

Applicable to embedded and deep learning models.

Improves scalability in high-dimensional data analysis.

Abstract

Intelligent test requires efficient and effective analysis of high-dimensional data in a large scale. Traditionally, the analysis is often conducted by human experts, but it is not scalable in the era of big data. To tackle this challenge, variable selection has been recently introduced to intelligent test. However, in practice, we encounter scenarios where certain variables (e.g. some specific processing conditions for a device under test) must be maintained after variable selection. We call this conditional variable selection, which has not been well investigated for embedded or deep-learning-based variable selection methods. In this paper, we discuss a novel conditional variable selection framework that can select the most important candidate variables given a set of preselected variables.