By Topic

Data mining tools applied to high-voltage insulators

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
M. Mejia-Lavalle ; Instituto de Investigaciones Electricas ; G. Rodriguez-Ortiz ; G. Montoya-Tena

The integration of four artificial intelligence tools that combine the ID3 algorithm and the case-based reasoning method of the nearest neighbor are proposed to solve the problem of characterizing the flashover on high-voltage insulators. The first tool uses data mining to build a classification or decision tree from historic data, the second, generates production rules, the third, operates the decision tree as an expert system, and the last, makes tests with known cases to evaluate classification accuracy. These tools are applied to the high-voltage insulators flashover problem, and the results are compared against other two machine learning tools: C4.5 and CN2.

Published in:

ISAI/IFIS 1996. Mexico-USA Collaboration in Intelligent Systems Technologies. Proceedings

Date of Conference:

15-15 Nov. 1996