.png){kind=icon}
返回
Multi-label classification based on analog reasoning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.05.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Some of the real-world problems are represented with just one label but many of today's issues are currently being defined with multiple labels. This second group is important because multi-label classes provide a more global picture of the problem. From the study of the characteristics of the most influential systems in this area, MIKnn and RAkEL, we can observe that the main drawback of these specific systems is the time required. Therefore, the aim of the current paper is to develop a more efficient system in terms of computation without incurring accuracy loss. To meet this objective we propose MICBR, a system for multi-label classification based on Case-Based Reasoning. The results obtained highlight the strong performance of our algorithm in comparison with previous benchmark methods in terms of accuracy rates and computational time reduction. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Multi-label
Classification
Case-Based Reasoning
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:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
MASS SOCIOGENIC ILLNESS BY PROXY: PARENTALLY REPORTED EPIDEMIC IN AN ELEMENTARY SCHOOL.png)
The Lancet
The LancetIF0
PREAI
分享收藏Morphology and properties of melt-spun polycarbonate fibers containing single- and multi-wall carbon nanotubesPolymer
PolymerIF0
PREAI
分享收藏Physiological and behavioral responses to corticotropin-releasing factor administration: is CRF a mediator of anxiety or stress responses?促肾上腺皮质激素释放因子的生理和行为反应: CRF是焦虑或应激反应的中介物吗？
PREAI
分享收藏ML-KNN: A lazy learning approach to multi-label leamingMl-knn: 一种多标签学习的懒惰学习方法PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏分享收藏 没有更多内容
没有更多内容