Toward a Unifying Taxonomy for Feature Selection

The objective of this Small Grant for Exploratory Research (SGER) is to establish a unifying taxonomy of features selection. Feature selection is to choose a subset of features among the available ones. It can be viewed as an optimization problem of exponential time complexity along several dimensions. Many feature selection algorithms have been developed and systems deployed in real-world applications. However, there exists a distinct gap between what theory suggests and what practice reveals, and the proliferation of feature selection algorithms has not brought about a general methodology that facilitates new research and development on feature selection. This project is a first step toward dealing with the two issues. The task is accomplished in two steps: (1) defining a common platform to consider representative algorithms on the equal footing; and (2) building a unifying taxonomy to discover how they complement each other and what is missing. Representative data and algorithms are collected and comparative experiments are conducted during the project. The results of this project will be a contemporary survey, a unifying taxonomy of feature selection algorithms, and some potential solutions to the automatic selection problem - being able to automatically choose the most suitable feature selection algorithm with given conditions.

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This project is suported by National Science Foundation under Grant No. IIS-0127815. Any opinions, findings, and conclusions or recommendations expressed here are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Created on June 21, 2001 by Huan Liu who can be reached at

Last Upadted: Thursday, June 27, 2002