.png){kind=icon}
Return
Using J-pruning to reduce overfitting in classification trees
.png){kind=icon}
PRE
AI
DOI：10.1016/S0950-7051(01)00163-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The automatic induction of classification rules from examples in the form of a decision tree is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. In some cases this can lead to an excessively large number of rules, many of which have very little predictive value for unseen data. This paper is concerned with the reduction of overfitting during decision tree generation. It introduces a technique known as J-pruning, based on the J-measure, an information theoretic means of quantifying the information content of a rule. (C) 2002 Elsevier Science B,V. All rights reserved.
Keywords:
classification
data mining
rule induction
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:
1.3W
Citations:
4.5W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave