An implementation of a multivariate discretization for supervised learning using Forestdisc

Maissae Haddouchi, A. Berrado
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引用次数: 1

Abstract

Discretization is a key pre-processing step in Machine Learning that transforms continuous attributes into discrete ones, through different methods available in the literature. In this regard, this work provides the ForestDisc framework that discretizes data based on a supervised, multivariate and hybrid approach. It uses, at first, a splitting process relying on a tree learning ensemble to generate a large set of cut points. It then uses a merging process based on moment matching optimization, to transform this set into a reduced and representative one. ForestDisc is a non-parametric discretizer in the sense that it does not require the user to introduce any initial setting parameters. We implemented ForestDisc algorithm in the "ForestDisc" R package.
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用Forestdisc实现监督学习的多元离散化
离散化是机器学习中关键的预处理步骤,通过文献中可用的不同方法将连续属性转换为离散属性。在这方面,这项工作提供了基于监督、多元和混合方法离散数据的ForestDisc框架。首先,它使用一个依赖于树学习集成的分裂过程来生成一个大的切点集。然后利用基于矩匹配优化的归并过程,将该集合转化为约简后的具有代表性的集合。ForestDisc是一种非参数离散器,它不需要用户引入任何初始设置参数。我们在“ForestDisc”R包中实现了ForestDisc算法。
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