Recent advances in feature selection and its applications

被引：1
作者：
Yun Li
Tao Li
Huan Liu
机构：
[1] Nanjing University of Posts and Telecommunications,School of Computer Science and Technology
[2] Nanjing University of Posts and Telecommunications,Jiangsu Key Laboratory of Big Data Security and Intelligent Processing
[3] Florida International University,School of Computer Science
[4] Arizona State University,School of Computing, Informatics, and Decision Systems Engineering
来源：
Knowledge and Information Systems | 2017年 / 53卷
关键词：
Feature selection; Survey; Data mining;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Feature selection is one of the key problems for machine learning and data mining. In this review paper, a brief historical background of the field is given, followed by a selection of challenges which are of particular current interests, such as feature selection for high-dimensional small sample size data, large-scale data, and secure feature selection. Along with these challenges, some hot topics for feature selection have emerged, e.g., stable feature selection, multi-view feature selection, distributed feature selection, multi-label feature selection, online feature selection, and adversarial feature selection. Then, the recent advances of these topics are surveyed in this paper. For each topic, the existing problems are analyzed, and then, current solutions to these problems are presented and discussed. Besides the topics, some representative applications of feature selection are also introduced, such as applications in bioinformatics, social media, and multimedia retrieval.
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收藏
页码：551 / 577
页数：26
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