By Topic

Fuzzy SLIQ Decision Tree Algorithm

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)
Chandra, B. ; Indian Inst. of Technol. Delhi, Delhi ; Varghese, P.P.

Traditional decision tree algorithms face the problem of having sharp decision boundaries which are hardly found in any real-life classification problems. A fuzzy supervised learning in Quest (SLIQ) decision tree (FS-DT) algorithm is proposed in this paper. It is aimed at constructing a fuzzy decision boundary instead of a crisp decision boundary. Size of the decision tree constructed is another very important parameter in decision tree algorithms. Large and deeper decision tree results in incomprehensible induction rules. The proposed FS-DT algorithm modifies the SLIQ decision tree algorithm to construct a fuzzy binary decision tree of significantly reduced size. The performance of the FS-DT algorithm is compared with SLIQ using several real-life datasets taken from the UCI Machine Learning Repository. The FS-DT algorithm outperforms its crisp counterpart in terms of classification accuracy. FS-DT also results in more than 70% reduction in size of the decision tree compared to SLIQ.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:38 ,  Issue: 5 )