SGER: Toward a Unifying Taxonomy for Feature Selection

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The objective of this Small Grant for Exploratory Research (SGER) project is to establish a unifying taxonomy of features selection. Feature selection is used in used in various applications, including pattern recognition, machine learning, datamining or decision making, to choose the most appropriate subset of features among the available ones for the task. It can be viewed as an optimization problem of exponential time complexity along several dimensions. Many feature selection algorithms have been developed and 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 makes it very difficult to fully understand the various feature selection techniques and construct a general methodology for feature selection. It is time-critical that these issues are addressed and a unifying taxonomy is developed, to facilitate new research, development and tools in feature selection. This project explores the first step toward dealing with these issues. The task of establishing a unifying taxonomy for feature selection 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 the algorithms complement each other and what is missing. The approach includes collection of representative data and algorithms and conducting comparative experiments to determine the characteristics of the feature selection algorithms, their performance on different data and tasks. The expected results of this project include 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 the problem conditions. The progress and updates of the project, and the resulting survey and unifying taxonomy will be available online.