Rough set-based empirical modeling with applications to control, pattern classification adn data mining

基于粗糙集的经验建模及其应用于控制、模式分类和数据挖掘

• 批准号: 2519-2006
• 负责人: Ziarko, Wojciech
• 金额: $ 1.38万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=388616
• 关键词: Rough; set; based; empirical; modeling

The proposed research program is concerned with the fundamental research, algorithms and applications of rough set theory and its probabilistic extensions. The theory of rough sets and the related technologies deal in the most part with automated learning of classification algorithms from data. The rough set-based methods include techniques for the analysis of suitability of data for the learning purpose, techniques for the elimination of  useless information from data, while focusing on the most dominant classificatory factors, and   the techniques for automated formation of classification rules and decision tables from data.      The current proposal is focused on probabilistic approaches to rough sets, in particular on variable precision and probabilistic  rough set models. The probabilistic extensions allow to model stochastic relationships existing in data and to develop classifier systems operating with controlled  degree of uncertainty.      The application aspect of the proposal is focused on three major application domains. The first one is related to the theory, algorithms and experimental investigation of the problem of control algorithm acquisition from data through  machine learning. The key objective in this process is to substitute the complex mathematical modeling steps with automated generation of control algorithm from operation data.      The second application domain deals with the  methodology, algorithms and prototyping of selected pattern recognition applications. In particular, experiments will be conducted with  automated image classification such as medical image interpretation, face image recognition, text contents classification and  speaker-independent recognition of isolated spoken words.      The third broad application area is data mining in which the rough set-based algorithms will be used for analysis and modeling of probabilistic dependencies existing in data, detection of fundamental factors in the relationships and optimization of the derived models. The main focus will be on analysis and interpretation of medical data in cooperation with medical research institutions.

该研究计划涉及粗糙集理论及其概率扩展的基础研究，算法和应用。粗糙集理论和相关技术主要涉及从数据中自动学习分类算法。基于粗糙集的方法包括用于分析数据对于学习目的的适用性的技术，用于从数据中消除无用信息的技术，同时关注最主要的分类因素， 从数据中自动形成分类规则和决策表的技术。 目前的建议是集中在粗糙集的概率方法，特别是可变精度和概率粗糙集模型。概率扩展允许对数据中存在的随机关系进行建模，并开发具有受控不确定性程度的分类器系统。 该提案的应用方面侧重于三个主要应用领域。第一个是关于通过机器学习从数据中获取控制算法问题的理论、算法和实验研究。在这个过程中的关键目标是取代复杂的数学建模步骤与自动生成的控制算法从操作数据。 第二个应用领域涉及所选模式识别应用的方法、算法和原型。特别是，实验将进行自动图像分类，如医学图像解释，人脸图像识别，文本内容分类和孤立的口语单词的说话者独立识别。 第三个广泛的应用领域是数据挖掘，其中基于粗糙集的算法将用于分析和建模的概率依赖关系存在于数据中，检测的基本因素的关系和优化的派生模型。主要重点将是与医学研究机构合作分析和解释医学数据。