最小冗余特征选择
冗余(工程)
特征选择
计算机科学
相关性(法律)
特征(语言学)
数据挖掘
人工智能
光学(聚焦)
模式识别(心理学)
机器学习
哲学
法学
物理
光学
操作系统
语言学
政治学
摘要
Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature selection of high-dimensional data. We define feature redundancy and propose to perform explicit redundancy analysis in feature selection. A new framework is introduced that decouples relevance analysis and redundancy analysis. We develop a correlation-based method for relevance and redundancy analysis, and conduct an empirical study of its efficiency and effectiveness comparing with representative methods.
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