An empirical analysis of decision tree algorithms: Modeling hepatitis data

机译：决策树算法的实证分析：肝炎数据建模

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Data mining refers to the process of retrieving knowledge by discovering patterns from large datasets. This paper highlights the performance of seven decision tree classification algorithms viz. Decision Stump, Hoeffding Tree, J48, Logistic Model Tree(LMT), Random Forest, REP (Reduced Error Pruning) Tree and Random Tree on the Hepatitis prognostic dataset that enables the classifier to accurately carry out categorization of medical data. The classification accuracies are evaluated using 10 fold cross validation technique. The results affirm the fact that the Random Forest algorithm better performs all other algorithms.

机译：数据挖掘是指通过发现来自大型数据集的模式来检索知识的过程。本文突出了七种决策树分类算法的性能。决策树桩，Hoeffd树，J48，Lopistic模型树（LMT），随机森林，rep（减少错误修剪）树和随机树在肝炎预后数据集上，使分类器能够准确地执行医疗数据的分类。使用10倍交叉验证技术进行评估分类精度。结果确认了随机林算法更好地执行所有其他算法的事实。

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《International Conference on Engineering Technology》|2015年||共4页
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Ramasamy Manickam; Selvaraj Shanthi; Mayilvaganan M.;
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正文语种
中图分类计算技术、计算机技术;
关键词
Classification; Data Mining; Decision Tree; Hepatitis Data Set;

机译：分类;数据挖掘;决策树;肝炎数据集;

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4. An empirical analysis of decision tree algorithms: Modeling hepatitis data [C] . Ramasamy Manickam, Selvaraj Shanthi, Mayilvaganan M. 2015 IEEE International Conference on Engineering amp; Technology . 2015

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An empirical analysis of decision tree algorithms: Modeling hepatitis data

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