Extracting Credible Dependencies for Averaged One-Dependence Estimator Analysis

LiMin Wang, ShuangCheng Wang, XiongFei Li and BaoRong Chi

Mathematical Problems in Engineering, 2014, vol. 2014, 1-11

Abstract:

Of the numerous proposals to improve the accuracy of naive Bayes (NB) by weakening the conditional independence assumption, averaged one-dependence estimator (AODE) demonstrates remarkable zero-one loss performance. However, indiscriminate superparent attributes will bring both considerable computational cost and negative effect on classification accuracy. In this paper, to extract the most credible dependencies we present a new type of seminaive Bayesian operation, which selects superparent attributes by building maximum weighted spanning tree and removes highly correlated children attributes by functional dependency and canonical cover analysis. Our extensive experimental comparison on UCI data sets shows that this operation efficiently identifies possible superparent attributes at training time and eliminates redundant children attributes at classification time.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2014/470821.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2014/470821.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:470821

DOI: 10.1155/2014/470821

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().