新的多元决策树构造启发式方法

M. Amasyali, O. Ersoy
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引用次数: 6

摘要

决策树常用于模式识别和回归问题。它们因高性能和易于理解的规则而具有吸引力。由于决策树构造算法的普及,人们开发了许多不同的决策树构造算法。在这项工作中,我们描述了一些新的启发式树构建算法,并在8个基准数据集上进行了测试。我们将新方法与其他21种树归纳算法进行了比较。结果表明,线性启发式算法具有简单、可接受的性能,可用于各种类型的分类问题
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Cline: new multivariate decision tree construction heuristics
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
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