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.

研究高维数据集中的特征选择方法及其发展，包括单变量相关性排名算法、关联 - 冗余折衷和基于多变量依赖性的方法，并总结文献中各种方法的贡献，同时介绍当前的问题和挑战，以确定未来的研究和发展方向。