Feature Selection: A perspective on inter-attribute cooperation
This is an incremental survey paper that reviews existing methods for feature selection, targeting researchers in data mining and machine learning dealing with dimensionality reduction.
The paper provides a comprehensive survey of filter feature selection methods that leverage inter-attribute cooperation to address high-dimensional data challenges in machine learning, summarizing state-of-the-art approaches and identifying future research directions.
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.