# Conditional Variable Selection for Intelligent Test

arXiv: https://arxiv.org/abs/2207.00335 · HTML: https://mlsanity.com/paper/2207.00335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization](https://mlsanity.com/paper/2010.13631/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deep Feature Selection Using a Novel Complementary Feature Mask](https://mlsanity.com/paper/2209.12282/) (2022)

## Abstract (arXiv)

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.