Cart (Loading....) | Create Account
Close category search window

A Hybrid Evolutionary Approach To Construct Optimal Decision Trees With Large Data Sets

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
$31 $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

2 Author(s)
Patil, D.V. ; S.G.G.S. Inst. of Eng. & Tech. Nanded.M.S, Maharashtra ; Bichkar, R.S.

Data mining environments produces large volume of data. The large amount of knowledge contains can be utilized to improve decision-making process of an organization. Large amount of available data when used for decision tree construction builds large sized trees that are incomprehensible to human experts. The learning process on this high volume data becomes very slow, as it has to be done serially on available large datasets. Our ultimate goal is to build smaller trees with equally accurate solutions with randomly selected sampled data. We experimented on techniques based on the idea of incremental random sampling combined with genetic algorithms that uses global search techniques to evolve decision Trees to obtain compact representation of large data set. Experiments performed on some data sets proved that the proposed random sampling procedures with genetic algorithms to build decision Trees gives relatively smaller trees as compared to other methods but equally accurate solution as other methods. The method incorporates optimization with the comprehensibility and scalability. We tried to explore the method using that we can avoid problems like slow execution, overloading of memory and processor with very large database can be avoided using the technique.

Published in:

Industrial Technology, 2006. ICIT 2006. IEEE International Conference on

Date of Conference:

15-17 Dec. 2006

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.