学习类别不平衡多标签分类的样本代表性

IF 3.7 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Analysis and Applications Pub Date : 2024-02-28 DOI:10.1007/s10044-024-01209-8

Yu Zhang, Sichen Cao, Siya Mi, Yali Bian

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Learning sample representativeness for class-imbalanced multi-label classification

查看原文本刊更多论文

Learning sample representativeness for class-imbalanced multi-label classification

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Pattern Analysis and Applications 工程技术-计算机：人工智能

CiteScore

7.40

自引率

2.60%

发文量

审稿时长

13.5 months

期刊介绍： The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.