.png){kind=icon}
Return
Multi-label classification based on analog reasoning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.05.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Multi-label
Classification
Case-Based Reasoning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
Cited Papers
ShareSaveMASS SOCIOGENIC ILLNESS BY PROXY: PARENTALLY REPORTED EPIDEMIC IN AN ELEMENTARY SCHOOL.png)
The Lancet
The LancetIF0
PREAI
ShareSaveMorphology and properties of melt-spun polycarbonate fibers containing single- and multi-wall carbon nanotubesPolymer
PolymerIF0
PREAI
ShareSave
ShareSave no more
no more