Fuzzy SLIQ Decision Tree Algorithm

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 decisi...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 38; no. 5; pp. 1294 - 1301
Main Authors: Chandra, B., Varghese, P.P.
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2008
Subjects:
Algorithms
Boundaries
Classification
Classification tree analysis
Clouds
Computer Simulation
Construction
Crisps
Decision Support Techniques
Decision trees
Fuzzy
fuzzy decision tree
Fuzzy Logic
fuzzy membership function
Fuzzy set theory
Gini index
Machine learning
Machine learning algorithms
Models, Theoretical
Partitioning algorithms
Pattern classification
Pattern Recognition, Automated - methods
Supervised learning
fuzzy decision tree
fuzzy membership function
Gini index
Classification
ISSN:1083-4419, 1941-0492, 1941-0492
Online Access:Get full text
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Summary: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.
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ISSN:1083-4419
1941-0492
1941-0492
DOI:10.1109/TSMCB.2008.923529