A survey on feature selection methods for mixed data

Feature Selection for mixed data is an active research area with many applications in practical problems where numerical and non-numerical features describe the objects of study. This paper provides the first comprehensive and structured revision of the existing supervised and unsupervised feature selection methods for mixed data reported in the literature. Additionally, we present an analysis of the main characteristics, advantages, and disadvantages of the feature selection methods reviewed in this survey and discuss some important open challenges and potential future research opportunities in this field.

Automated summary

This paper provides a comprehensive review of existing supervised and unsupervised feature selection methods for mixed data, analyzing their characteristics, advantages, and disadvantages, and discussing important challenges and potential future research opportunities in this field.