By Topic

Multivariate decision trees using linear discriminants and tabu search

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

6 Author(s)
Xiao-Bai Li ; Sch. of Manage., Univ. of Texas, Richardson, TX, USA ; Sweigart, J.R. ; Teng, J.T.C. ; Donohue, J.M.
more authors

A new decision tree method for application in data mining, machine learning, pattern recognition, and other areas is proposed in this paper. The new method incorporates a classical multivariate statistical method, linear discriminant function, into decision trees' recursive partitioning process. The proposed method considers not only the linear combination with all variables, but also combinations with fewer variables. It uses a tabu search technique to find appropriate variable combinations within a reasonable length of time. For problems with more than two classes, the tabu search technique is also used to group the data into two superclasses before each split. The results of our experimental study indicate that the proposed algorithm appears to outperform some of the major classification algorithms in terms of classification accuracy, the proposed algorithm generates decision trees with relatively small sizes, and the proposed algorithm runs faster than most multivariate decision trees and its computing time increases linearly with data size, indicating that the algorithm is scalable to large datasets.

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:33 ,  Issue: 2 )