.png){kind=icon}
返回
Feature selection: a perspective on inter-attribute cooperation
.png){kind=icon}
OA
AI
DOI：10.1007/s41060-023-00439-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a learning algorithm. Over the decades, filter feature selection methods have evolved from simple univariate relevance ranking algorithms to more sophisticated relevance-redundancy trade-offs and to multivariate dependencies-based approaches in recent years. This tendency to capture multivariate dependence aims at obtaining unique information about the class from the intercooperation among features. This paper presents a comprehensive survey of the state-of-the-art work on filter feature selection methods assisted by feature intercooperation, and summarizes the contributions of different approaches found in the literature. Furthermore, current issues and challenges are introduced to identify promising future research and development.
Keyword:
Filter feature selection
Information-theoretic measures
High-order dependency
Feature intercooperation
Interaction
Synergy
Complementarity
期刊
I
IF:
2.8
论文数:
1.1K
被引数:
1.3K
机构
引用论文
分享收藏Studies in the flexibility of macrocyclic ligands. Crystal and molecular structure of 2,13-dimethy1-3,6,9,12,18-pentaazabicyclo (12.3.1) octadeca-(18),14,16-triene-dichloroion (III) hexafluorophosphate.png)
Polyhedron
PolyhedronIF0
PREAI
分享收藏分享收藏Theoretical foundations of forward feature selection methods based on mutual informationNEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Selecting feature subset for high dimensional data via the propositional FOIL rules通过命题箔规则选择高维数据的特征子集PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容