Toward integrating feature selection algorithms for classification and clustering
Toward integrating feature selection algorithms for classification and clustering
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DOI：
10.1109/tkde.2005.66
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发表时间：
2005-04-01
影响因子：
8.9
通讯作者：
Yu, L
中科院分区：
文献类型：
--
作者：
Liu, H;Yu, L
本文介绍了特征选择的概念和算法，调查现有的特征选择算法的分类和聚类，组和比较不同的算法与分类框架的基础上的搜索策略，评估标准，和数据挖掘任务，揭示未尝试的组合，并提供指导方针，在选择特征选择算法。在分类框架的基础上，我们继续努力构建一个智能特征选择的集成系统。建议作为中间步骤建立一个统一的平台。一个说明性的例子，以显示如何现有的特征选择算法可以集成到一个Meta算法，可以利用个人的算法。这样做的附加优点是帮助用户采用合适的算法而无需知道每个算法的细节。一些现实世界的应用程序，包括演示使用的特征选择在数据挖掘。最后，我们确定的趋势和挑战的特征选择的研究和发展这项工作。
This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms. An added advantage of doing so is to help a user employ a suitable algorithm without knowing details of each algorithm. Some real-world applications are included to demonstrate the use of feature selection in data mining. We conclude this work by identifying trends and challenges of feature selection research and development.