.png){kind=icon}
返回
Dynamic relevance and interdependent feature selection for continuous data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.116302.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature Selection (FS) techniques are employed in expert system for selecting relevant and eliminating redundant features. Information-theoretic based FS methods measure non-linear correlation and have gained popularity recently. However, these methods work with discrete data, leading to an additional overhead of discretization while dealing with continuous data. In order to resolve this, the paper proposes a FS method namely, Dynamic Relevance Interdependent Feature Selection (DRIFS) for continuous data which retains the essence of information-theoretic based methods. DRIFS incorporates two major aspects of FS that are ignored by many state of the art methods. First, the method updates the relevance of every candidate features after a new feature is selected and second, the method discriminates between redundant and interdependent features. The performance of DRIFS is compared with six other FS methods, CIFE, CMIM, DISR, ICAP, IGFS and MRMR. Ten real world datasets, which include seven high-dimensional microarray datasets are used for experimental validation. DRIFS performs better in terms of classification accuracy and the number of feature selected. Experimental results also show an improvement in classification accuracy after the application of DRIFS.
Keyword:
Feature selection
Dynamic relevance
Interdependent features
Redundant features
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
Particle robotics based on statistical mechanics of loosely coupled components基于松散耦合构件统计力学的粒子机器人技术.png)
Nature
NatureIF0
PREAI
分享收藏分享收藏A novel feature selection method considering feature interaction一种考虑特征交互的特征选择方法PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容