Feature Selection: A perspective on inter-attribute cooperation

TL;DR

This paper surveys advanced filter feature selection methods that leverage inter-attribute cooperation to improve dimensionality reduction in high-dimensional datasets, highlighting recent progress and future challenges.

Contribution

It provides a comprehensive overview of state-of-the-art filter feature selection techniques based on feature intercooperation and discusses future research directions.

Findings

Multivariate dependence approaches capture inter-feature cooperation.

Recent methods improve relevance-redundancy trade-offs.

Challenges include scalability and robustness of methods.

Abstract

High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a learning algorithm. Over the decades, filter feature selection methods have evolved from simple univariate relevance ranking algorithms to more sophisticated relevance-redundancy trade-offs and to multivariate dependencies-based approaches in recent years. This tendency to capture multivariate dependence aims at obtaining unique information about the class from the intercooperation among features. This paper presents a comprehensive survey of the state-of-the-art work on filter feature selection methods assisted by feature intercooperation, and summarizes the contributions of different approaches found in the literature. Furthermore, current issues and challenges are introduced to identify promising future research and development.