SGER: Toward a Unifying Taxonomy for Feature Selection

SGER：迈向特征选择的统一分类法

• 批准号: 0127815
• 负责人: Huan Liu
• 金额: $ 5.5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0127815&HistoricalAwards=false
• 关键词: SGER; Toward; Unifying; Taxonomy; Feature

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.

这个小型探索性研究拨款(SGER)项目的目标是建立一个统一的特征选择分类法。特征选择用于各种应用中，包括模式识别、机器学习、数据挖掘或决策，以从可用特征子集中选择最适合该任务的特征子集。它可以看作是一个在多个维度上具有指数时间复杂度的优化问题。许多特征选择算法已经被开发并部署在现实世界的应用中。然而，理论建议和实践揭示之间存在着明显的差距，特征选择算法的激增使得充分理解各种特征选择技术和构建通用的特征选择方法变得非常困难。解决这些问题并制定统一的分类法，以促进特征选择方面的新研究、开发和工具，这在时间上至关重要。这个项目探索了处理这些问题的第一步。建立用于特征选择的统一分类法的任务分两步完成：(1)定义一个公共平台以平等地考虑代表性算法；(2)建立统一分类法，以发现算法如何互补和缺失。该方法包括收集具有代表性的数据和算法，并进行对比实验，以确定特征选择算法的特点，以及它们在不同数据和任务上的性能。这个项目的预期结果包括一个当代的调查，一个统一的特征选择算法分类，以及一些潜在的自动选择问题的解决方案--能够在给定的问题条件下自动选择最合适的特征选择算法。该项目的进展和更新，以及由此产生的调查和统一的分类法将在网上获得。