.png){kind=icon}
.png){kind=icon}
Return
Attribute weighting for averaged one-dependence estimators
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-016-0854-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Averaged one-dependence estimators (AODE) is a type of supervised learning algorithm that relaxes the conditional independence assumption that governs standard naive Bayes learning algorithms. AODE has demonstrated reasonable improvement in terms of classification performance when compared with a naive Bayes learner. However, AODE does not consider the relationships between the super-parent attribute and other normal attributes. In this paper, we propose a novel method based on AODE that weighs the relationship between the attributes called weighted AODE (WAODE), which is an attribute weighting method that uses the conditional mutual information metric to rank the relations among the attributes. We have conducted experiments on University of California, Irvine (UCI) benchmark datasets and compared accuracies between AODE and our proposed learner. The experimental results in our paper show that WAODE exhibits higher accuracy performance than the original AODE.
Keywords:
Attribute weight
Structure extension
Conditional mutual information
Averaged one-dependence estimators
Bayesian model
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.5K
Citations:
1.7W