Discretization: An enabling technique
Discretization: An enabling technique
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DOI：
10.1023/a:1016304305535
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发表时间：
2002-10-01
影响因子：
4.8
通讯作者：
Dash, M
中科院分区：
文献类型：
--
作者：
Liu, H;Hussain, F;Dash, M
离散值在数据挖掘和知识发现中发挥着重要作用。它们是关于数字区间的，这些数字的表示和指定更简洁，更易于使用和理解，因为它们比连续值更接近知识水平表示。许多研究表明归纳任务可以从离散化中受益：具有离散值的规则通常更短且更容易理解，并且离散化可以提高预测准确性。此外，文献中发现的许多归纳算法都需要离散特征。所有这些都促使研究人员和从业者在机器学习或数据挖掘任务之前或期间离散化连续特征。文献中有许多可用的离散化方法。现在是时候让我们检查这些看似不同的离散化方法，并找出它们到底有多么不同，离散化过程的关键组成部分是什么，我们如何提高当前的研究水平以进行新的开发以及现有方法的使用。本文旨在系统研究离散化方法的发展历史、对分类的影响以及速度和精度之间的权衡。本文的贡献是对现有离散化方法的抽象描述，对现有方法进行分类并为进一步发展铺平道路的层次框架，对代表性离散化方法的简明讨论，广泛的实验及其分析，以及在各种情况下如何选择离散化方法的一些指导。我们还确定了一些尚未解决的问题以及离散化的未来研究。
Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers which are more concise to represent and specify, easier to use and comprehend as they are closer to a knowledge-level representation than continuous values. Many studies show induction tasks can benefit from discretization: rules with discrete values are normally shorter and more understandable and discretization can lead to improved predictive accuracy. Furthermore, many induction algorithms found in the literature require discrete features. All these prompt researchers and practitioners to discretize continuous features before or during a machine learning or data mining task. There are numerous discretization methods available in the literature. It is time for us to examine these seemingly different methods for discretization and find out how different they really are, what are the key components of a discretization process, how we can improve the current level of research for new development as well as the use of existing methods. This paper aims at a systematic study of discretization methods with their history of development, effect on classification, and trade-off between speed and accuracy. Contributions of this paper are an abstract description summarizing existing discretization methods, a hierarchical framework to categorize the existing methods and pave the way for further development, concise discussions of representative discretization methods, extensive experiments and their analysis, and some guidelines as to how to choose a discretization method under various circumstances. We also identify some issues yet to solve and future research for discretization.