DocumentCode :
2341087
Title :
Cline: new multivariate decision tree construction heuristics
Author :
Amasyali, M. Fatih ; Ersoy, Okan
Author_Institution :
Dept. of Comput. Eng., Yildiz Tech. Univ., Istanbul
fYear :
0
fDate :
0-0 0
Abstract :
Decision trees are often used in pattern recognition and regression problems. They are attractive due to high performance and easy-to-understand rules. Many different decision tree construction algorithms have been developed because of their popularity. In this work, we describe some new heuristic tree construction algorithms and test with 8 benchmark datasets. We compare the new method with other 21 tree induction algorithms. The results show that cline heuristics can be used in all types of classification problems because of its simplicity and acceptable performance
Keywords :
decision trees; Cline; decision tree construction algorithms; heuristic tree construction algorithms; multivariate decision tree construction heuristics; pattern recognition; regression problems; tree induction algorithms; Benchmark testing; Binary trees; Classification tree analysis; Decision making; Decision trees; Machine learning; Machine learning algorithms; Navigation; Pattern recognition; Regression tree analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence Methods and Applications, 2005 ICSC Congress on
Conference_Location :
Istanbul
Print_ISBN :
1-4244-0020-1
Type :
conf
DOI :
10.1109/CIMA.2005.1662359
Filename :
1662359
Link To Document :
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