.png){kind=icon}
.png){kind=icon}
Return
Rough set based approach for inducing decision trees
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2006.10.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, two concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced. The authors discuss the differences between the rough set based approaches and the fundamental entropy based method. The comparison between the presented approach and the rough set based approach and the fundamental entropy based method on some data sets from the UCI Machine Learning Repository is also reported. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
variable precision rough set model
variable precision explicit region
variable precision implicit region
machine learning and decision tree
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W