Breeding Decision Trees Using Evolutionary Techniques
Breeding Decision Trees Using Evolutionary Techniques
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使用进化技术培育决策树
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Dimitris Kalles
中科院分区：
文献类型：
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作者：
Athanassios Papagelis;Dimitris Kalles
我们探索使用遗传算法直接进化分类决策树。我们认为这样的概念学习器的适用性，由于其能够有效地搜索复杂的假设空间，发现有条件的依赖以及不相关的属性。该系统的性能进行了测量的一组人工和标准的离散概念学习问题，并与两个已知的算法（C4.5，OneR）的性能进行比较。我们证明，标准算法的推导假设可以大大偏离最佳。这种偏差部分是因为他们的非通用程序偏见，它可以减少使用全球指标的树质量，如一个建议。
We explore the use of genetic algorithms to directly evolve classification decision trees. We argue on the suitability of such a concept learner due to its ability to efficiently search complex hypotheses spaces and discover conditionally dependent as well as irrelevant attributes. The performance of the system is measured on a set of artificial and standard discretized concept-learning problems and compared with the performance of two known algorithms (C4.5, OneR). We demonstrate that the derived hypotheses of standard algorithms can substantially deviate from the optimum. This deviation is partly because of their non-universal procedural bias and it can be reduced using global metrics of tree quality like the one proposed.