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A Rough Set-based Multiple Criteria Linear Programming Approach For The Medical Diagnosis And Prognosis

机译：基于粗糙集的多准则线性规划方法在医学诊断和预后中的应用

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摘要

It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, and support vector machine. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided, respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows system platforms. Finally, many experiments show that the RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining, and remarkably improve the accuracy of medical diagnosis and prognosis simultaneously.
机译：众所周知，数据挖掘是从大量数据中发现未知隐藏信息，提取有价值信息并使用该信息制定重要业务决策的过程。数据挖掘已发展成为一种新的信息技术，包括回归，决策树，神经网络，模糊集，粗糙集和支持向量机。提出了一种基于粗糙集的多准则线性规划（RS-MCLP）方法来解决数据挖掘中的分类问题。首先，我们描述了粗糙集和多准则线性规划（MCLP）的基本理论和模型，并分析了它们的特点和在实际应用中的优势。其次，分别提供了有关其缺陷的详细分析。然而，由于它们之间存在着相互的互补性，我们提出并建立了RS-MCLP方法和模型，它们充分融合了它们的优点，同时克服了不利因素。此外，我们还在SAS和Windows系统平台中开发和实现这些算法和模型。最后，许多实验表明，RS-MCLP方法在数据挖掘中先于单个MCLP模型和其他传统分类方法，并且显着提高了医疗诊断和预后的准确性。

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