Analysis of Ramer-Douglas-Peucker algorithm as a discretization method

机译：ramer-douglas-peucker算法分析为离散化方法

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Discretization is the process of converting continuous values into discrete values. It is crucial for several machine learning and data mining algorithms as certain algorithms work only on discrete values. In this study we investigate applicability of Ramer-Douglas-Peucker (RDP) algorithm as a discretization method. Experimental results demonstrate that RDP-based discretization achieves similar or better classification accuracy compared to equal width, equal frequency, Zeta and 1R.

机译：离散化是将连续值转换为离散值的过程。对于几种机器学习和数据挖掘算法至关重要，因为某些算法仅在离散值上工作。在这项研究中，我们研究了ramer-douglas-peucker（RDP）算法作为离散化方法的适用性。实验结果表明，与等宽度，等频率，Zeta和1R相比，基于RDP的离散化实现了类似或更高的分类精度。

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《Signal Processing and Communications Applications Conference》|2018年|672p|共4页
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Furkan Goz; Alev Mutlu; Orhan Akbulut;
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中图分类 TN911.7-53;
关键词
Machine learning algorithms; Machine learning; Iris; Indexes; Data mining; Heart; Knowledge discovery;

机译：机器学习算法;机器学习;虹膜;索引;数据挖掘;心脏;知识发现;

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4. Analysis of Ramer-Douglas-Peucker algorithm as a discretization method [C] . Furkan Goz, Alev Mutlu, Orhan Akbulut Signal Processing and Communications Applications Conference . 2018

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Analysis of Ramer-Douglas-Peucker algorithm as a discretization method

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