Conditional Variable Selection for Intelligent Test
This addresses a practical issue in intelligent test for high-dimensional data analysis, but it appears incremental as it extends existing variable selection methods to handle conditional constraints.
The paper tackles the problem of conditional variable selection in intelligent test, where certain variables must be maintained after selection, and proposes a novel framework that selects important candidate variables given a set of preselected variables, though no concrete results or numbers are provided.
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.