Description: 文本分类综述和一些近期基于粗糙集的文本分类文章，其中一篇国防科技大学的关于文本分类的文章非常好，另外还有一篇清华大学博士毕业论文（关于机器学习文本分类）对毕业设计也是很有指导作用的。-Summary of text classification and some recent Rough set-based text classification article, of which a National University of Defense Technology of Text Classification article very good, as well as a doctoral thesis, Tsinghua University (on machine learning text classification) on the graduation project is also very guiding role. Platform: |
Size: 26036224 |
Author:willee |
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Description: 粗糙集是一种数据预处理算法，可以用遗传算法对其进行优化。是很好的分类算法-Rough set is a data pre-processing algorithm, genetic algorithm can be used for its optimization. Is a good classification algorithm Platform: |
Size: 12288 |
Author:王淑娟 |
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Description: 粗糙集的综述，对于理解事物的分类很大的帮助。-Overview of rough set for understanding the classification of things of great help. Platform: |
Size: 136192 |
Author:zhangzhen |
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Description: Rose2里含有好多粗糙集的算法，可以实现数据预处理约简，求关联规则，求聚类和分类，非常实用。-Rose2 contains a lot of rough set algorithm, can achieve reduction of data preprocessing, find association rules, clustering and classification requirements, very practical. Platform: |
Size: 4145152 |
Author:曹盛文 |
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Description: 本文提出一种综合粗糙集与支持向量机的 Web 文本分类模型，利用粗糙集的属性约简方法，减少支持向量机训练数据的维数，提高 Web 文本分类的性能与效率.-This article advances a Web text classification model which synthesis rough set and support vector machine. Using the rough set’s attribute reduction method to reduce the dimension of support vector machine’s training data, then enhances the Web
text classification’s performance and efficiency. Platform: |
Size: 803840 |
Author:叶眸 |
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Description: A simple method of data classification using rough set theory is described, as implemented by the tools in Alexander Ohra s software application ROSETTA Platform: |
Size: 412672 |
Author:saomaket |
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Description: 引入能直接处理连续型数据的邻域粗糙集约简模型，给出一种基于邻域粗糙集模型和粒子群优化的特征选择算法。仿真实验结果表明该算法可以选择较少的特征，改善分类的能力。-employs the neighborhood rough set reduction model which can process the numerical features directly without discretization. Then the particle fitness function in particle swarm optimization (PSO) algorithm is built based on that model. Finally, a novel feature selection algorithm based on particle swarm optimization and neighborhood rough set reduction model is proposed. Experimental results prove that the new algorithm improves classification ability with fewer features selected. Platform: |
Size: 16384 |
Author:伍洁 |
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Description: 本书详尽地阐述了数据挖掘与知识发现领域中的一些基本理论和研究方法。介绍了KDD与数据挖掘的概念数据挖掘对象知识发现过程研究方法以及相关研究领域和应用范围。作为知识发现的数据预处理工作，简要叙述了数据清理、数据约简、数据概念等级分层、多维数据模型等内容。书中较详细地介绍了粗糙集、模糊集、聚类分析、关联规则、人工神经网络、分类与预测等数据挖掘方法，最后还简要介绍了多媒体数据挖掘工作的有关进展。 本书可以作为计算机科学与技术专业和信息科学方向高年级本科生和研究生的教材或参考书，也可供有关科技人员学习参考。
-The book detailed description of data mining and knowledge discovery in the field of some of the basic theory and research method. Introduces the concept of data mining KDD and data mining knowledge discovery process research methods and related research areas and applications. As a knowledge discovery data preprocessing, briefly describes the data cleaning, data reduction, data concept hierarchy, multidimensional data model etc.. The book describes in detail the rough set, fuzzy set, association rule, cluster analysis, artificial neural network, classification and prediction method of data mining, and also briefly introduces the multimedia data mining related work in progress. This book can serve as a professional computer science and technology and information science graduate and advanced undergraduate textbooks or reference books, is also available for study and reference about scientific and technical personnel. Platform: |
Size: 1608704 |
Author:冯荣俊 |
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Description: 提出一种基于粗糙集与支持向量机的客户动态评估方法。根据客户群特点从当前价值、潜在价值和附加价值三个维度分析并构建客户评估指标，利用指标的年增幅率监测客户价值的变化规律。应用粗糙集布尔推理算法、粒子群算法实现连续属性离散化和知识约简。通过10-重交叉验证和网格搜索技术获取最优惩罚因子与核参数，缩放样本数据集并完成支持向量机一对一分类器的训练与测试。结果表明该评估方法能够实现周期性的客户价值评估与细分，具有很强的泛化能力。- A customer dynamic evaluation method based on rough set and support vector machine is advanced. Customer evaluation indicators are analysed and established from three dimensions of current value, potential value and odditional value according to customer characteristics. The change rules of customer value are observed by annual increasing rate of indicators. Continuous attributes are discretized by rough set and boolean inference arithmetic. Redundant attributes are reduced by particle swarm optimization arithmetic. The optimal penalty factor and nuclear parameter are obtained by 10-cross validation and grid-search. The sample data scaling is carried out and the train and test of svm one against one classifier are accomplished. The result indicates that the evaluation method can not only implement the periodic evaluation and classification of customer value, but also have a better generalization. Platform: |
Size: 270336 |
Author:夏天 |
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Description: MATLAB粗糙集属性约简库（内含实例，原创）
函数M文件：
TargetOptFcn.m ------------ 遗传算法的目标函数
PositiveRegion.m --------- 计算正域
LowerApproximation.m ------ 计算下近似
IsSub.m ------------------- 判断集合A是否是集合B的子集
EquivalentClassSet.m ----- 基于R分类的所有等价类的集合，即U/R
EleEquivalentSet.m ------- 计算包含某元素的一个分类
DependencyDegree.m -------- 计算依赖度
-MATLAB rough set attribute reduction library (including examples, original)
The function M file:
TargetOptFcn.m---------------- The objective function of genetic algorithm
PositiveRegion.m-------------- Computing positive region
LowerApproximation.m---------- Calculation of approximation
IsSub.m----------------------- Judgment if set A is a subset of the set B
EquivalentClassSet.m---------- A collection of all the equivalence classes based on R classification, U/R
EleEquivalentSet.m------------ Calculation contains an assortment of an element
DependencyDegree.m------------ Dependence computation Platform: |
Size: 14336 |
Author:Neptune_zx |
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Description: 图像场景分类中视觉词包分类的应用与操作代码-Review of the bag-of-visual-words models in image scene classification
Platform: |
Size: 1112064 |
Author:libin |
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Description: 基于粗糙集的模糊分类模型的MATLAB代码。可用-MATLAB code based on fuzzy rough set classification model. Available Platform: |
Size: 13312 |
Author:testyi |
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Description: 粗糙集分类，使用MATLAB编程，做到了粗糙集分类器的实现-Rough set classification, the use of MATLAB programming, achieved the realization of the rough set classifier Platform: |
Size: 814080 |
Author:outlook.com |
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Description: 这是天津大学胡清华老师在粗糙集邻域领域做的最经典的源码，同学们可以在此基础上学习和修改，入口程序已经写好，需要其他方法可以自己添加，MAIN.m是入口程序，参数的意思在子函数里讲的很明白，调用了featureselect_FW_fast.m用来属性约简，几个clsf_dpd文件是使用不同的距离公式来计算属性重要度，选择得到属性结果，使用crossvalidate.m十折交叉算法来计算计算算法精度，该段代码调用了几个分类器，C4_5.m是决策树，KNN.m是最近邻分类器，NEC.m是类似于KNN的胡修改的程序，osu_svm3.00文件夹是使用svm分类器调用的文件，使用该分类器时，代码中间的路径需要修改。另外附上一堆常用的数据集。-This is Hu Qinghua teacher at Tianjin University neighborhood rough set field do the most classic source code, students can learn and modify On this basis, the entry procedures have been written, you need other ways to add your own, MAIN.m entry program is meaning parameters Functions talked in very clear call for the featureselect_FW_fast.m attribute reduction, several clsf_dpd file is to use a different formula to calculate the distance attribute importance, choose properties to get the results, use crossvalidate.m ten fold cross algorithm to calculate the accuracy of the calculation algorithm, the segment code calls several classifiers, C4_5.m is a decision tree, KNN.m is the nearest neighbor classifier, NEC.m is similar to KNN Hu modified program, osu_svm3. 00 folders using svm classifier called file, using the classification code in the middle of the path need to be modified. Also attach a bunch of common data sets. Platform: |
Size: 2542592 |
Author:robert |
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Description: we peresent full pakage of the methods that apply rough set theory in the
context of segmentation (or partitioning) of multichannel medical imaging
data. We put this approach into a semi-automatic framework, where
the user specifies the classes in the data by selecting respective regions in
2D slices. Rough set theory provides means to compute lower and upper
approximation of the classes. The boundary region between the lower
and the upper approximations represents the uncertainty of the classification.
We present an approach to automatically compute segmentation
rules the rough set classification using a k-means approach.-we peresent full pakage of the methods that apply rough set theory in the
context of segmentation (or partitioning) of multichannel medical imaging
data. We put this approach into a semi-automatic framework, where
the user specifies the classes in the data by selecting respective regions in
2D slices. Rough set theory provides means to compute lower and upper
approximation of the classes. The boundary region between the lower
and the upper approximations represents the uncertainty of the classification.
We present an approach to automatically compute segmentation
rules the rough set classification using a k-means approach. Platform: |
Size: 15299584 |
Author:Mohamed A. El-Sayed |
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Search - rough set classification