.png){kind=icon}
返回
Unsupervised Representative Feature Selection Algorithm Based on Information Entropy and Relevance Analysis
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2863752.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature selection plays an important role in preprocessing in pattern recognition and data mining, especially in large scale image, digital text, and biological data. Specifically, class label information is unavailable for conducting the selection of a minimal feature subset in unsupervised learning, which is full of challenging and interesting problems. In this paper, we present an unsupervised REPresentative Feature Selection (REPFS) algorithm based on information entropy and relevance analysis. The proposed method seeks to find a high-quality feature subset through feature clustering without using any learning algorithms. More importantly, the features' relevance will be computed based on an information metric of the relevance gain, which provides an information theoretical foundation for finding a minimum of the redundancy between features. Our results on nine benchmark data sets demonstrate that REPFS can significantly improve upon state-of-the-art unsupervised algorithms.
Keyword:
Feature selection
unsupervised learning
information entropy
relevance analysis
Markov blanket
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Camptothecin exhibits topoisomerase1-independent KMT1A suppression and myogenic differentiation in alveolar rhabdomyosarcoma cells.png)
OncotargetOAAI
OncotargetIF0
OAAI分享收藏Supervised Feature Selection With a Stratified Feature Weighting Method基于分层特征加权方法的监督特征选择IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏An efficient semi-supervised representatives feature selection algorithm based on information theoryPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏Self-representation based dual-graph regularized feature selection clusteringNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, ChinaWater Supply
OAAI
Water SupplyIF0
OAAI分享收藏
查看更多内容