.png){kind=icon}
返回
Differential evolution for feature selection: a fuzzy wrapper-filter approach
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-018-3545-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The selection of an optimal feature subset from all available features in the data is a vital task of data pre-processing used for several purposes such as the dimensionality reduction, the computational complexity reduction required for data processing (e.g., clustering, classification and regression) and the performance enhancement of a data processing technique. To serve such purposes, feature selection approaches which are fundamentally categorized into filters and wrappers try to eliminate irrelevant, redundant and erroneous features in the data. Each category comes with its own advantages and disadvantages. While wrappers can generally provide higher classification performance than filters, filters are computationally more efficient than wrappers. In order to bring the advantages of wrappers and filters together, i.e., to get higher classification performance with smaller feature subset size in a shorter time, this paper proposes a differential evolution approach combining filter and wrapper approaches through an improved information theoretic local search mechanism which is based on the concepts of fuzziness to cope with both continuous and discrete datasets. To show the superiority of the proposed approach, it is examined and compared with traditional and recent evolutionary feature selection approaches on several benchmarks from different well-known data repositories.
Keyword:
Differential evolution
Feature selection
Fuzzy mutual information
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:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Particle swarm optimization for parameter determination and feature selection of support vector machines粒子群优化算法在支持向量机参数确定与特征选择中的应用
PREAI
分享收藏
Pervasive Developmental Disorder Not Otherwise Specified: A Review and Guidelines for Clinical Care
PREAI
分享收藏分享收藏Application of global optimization methods to model and feature selection.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programmingCONNECTION SCIENCE
CONNECTION SCIENCEIF3.4
PREAI
分享收藏
查看更多内容