Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets"
Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets"
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DOI：
10.1016/j.ins.2014.07.029
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发表时间：
2014-12-10
影响因子：
8.1
通讯作者：
Manuel Benitez, Jose
中科院分区：
文献类型：
--
作者：
Septem Riza, Lala;Janusz, Andrzej;Manuel Benitez, Jose
RoughSets 包主要用 R 语言编写，提供粗糙集理论 (RST) 和模糊粗糙集理论 (FRST) 方法的实现，用于数据建模和分析。它不仅考虑基本概念（例如，不可辨别关系、下/上近似等），还考虑它们在许多任务中的应用：离散化、特征选择、实例选择、规则归纳和基于最近邻的分类器。提供包架构和示例是为了向研究人员和从业者介绍它。研究人员可以通过将自定义函数定义为参数来构建新模型，而从业者可以使用可用的算法对其数据进行分析和预测。此外，我们还提供了知名软件包的审查和比较。总的来说，我们的软件包应该被视为基于 RST 和 FRST 分析数据的替代软件库。 (C) 2014 Elsevier Inc. 保留所有权利。
The package RoughSets, written mainly in the R language, provides implementations of methods from the rough set theory (RST) and fuzzy rough set theory (FRST) for data modeling and analysis. It considers not only fundamental concepts (e.g., indiscernibility relations, lower/upper approximations, etc.), but also their applications in many tasks: discretization, feature selection, instance selection, rule induction, and nearest neighborbased classifiers. The package architecture and examples are presented in order to introduce it to researchers and practitioners. Researchers can build new models by defining custom functions as parameters, and practitioners are able to perform analysis and prediction of their data using available algorithms. Additionally, we provide a review and comparison of well-known software packages. Overall, our package should be considered as an alternative software library for analyzing data based on RST and FRST. (C) 2014 Elsevier Inc. All rights reserved.