Feature selection for high-dimensional data
Feature selection for high-dimensional data
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DOI：
10.1007/s13748-015-0080-y
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发表时间：
2016-05-01
影响因子：
4.2
通讯作者：
Alonso-Betanzos, Amparo
中科院分区：
文献类型：
--
作者：
Bolon-Canedo, Veronica;Sanchez-Marono, Noelia;Alonso-Betanzos, Amparo
本文给出了分类问题中特征选择的一种综合方法，解释了高维数据环境下特征选择的基础、实际应用问题和挑战。首先，我们重点介绍了特征选择的基础，回顾了特征选择的历史和基本概念。然后，我们讨论了特征选择起关键作用的不同主题，例如微阵列数据、入侵检测或医疗应用。最后，我们深入探讨了该领域的研究人员如果有兴趣面对“大数据”，更具体地说，“大维度”的到来，他们必须应对的开放挑战。
This paper offers a comprehensive approach to feature selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection in the context of high-dimensional data. First, we focus on the basis of feature selection, providing a review of its history and basic concepts. Then, we address different topics in which feature selection plays a crucial role, such as microarray data, intrusion detection, or medical applications. Finally, we delve into the open challenges that researchers in the field have to deal with if they are interested to confront the advent of "Big Data" and, more specifically, the "Big Dimensionality".