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
Cline (biology)
Decision tree
Computer science
Heuristics
Multivariate statistics
Heuristic
Hyperplane
Artificial intelligence
Machine learning
Mathematics
Combinatorics
Subject Areas
Rough Sets and Fuzzy Logic
·Computational Theory and Mathematics ·Physical Sciences
Multi-Criteria Decision Making
·Management Science and Operations Research ·Social Sciences
Advanced Statistical Methods and Models
·Statistics and Probability ·Physical Sciences
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