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datareductionwithroughsets
- 属性约简的matlab代码：实现了基于信息熵、模糊信息熵；这些算法可以同时处理离散变量和数值变量，无须离散化。
datareductionwithroughsets
- 属性约简的matlab代码：实现了基于信息熵、模糊信息熵；这些算法可以同时处理离散变量和数值变量，无须离散化。-Attribute Reduction of matlab code: to achieve based on information entropy, fuzzy information entropy these algorithms can deal with discrete variables and numeri
reduce
- 一种基于启发式算法(互信息熵)的粗糙集约简源代码-A heuristic algorithm based on (mutual information entropy) Rough Jane intensive source code
Entropyreduction
- 对于布尔属性决定信息增益,得出在给定规则的情况下的信息熵的平均约简-Boolean attribute information for the decision to add, come to the rules in a given case the average information entropy reduction
ImageEntropy1
- 这个例子主要用于计算一个图象的信息熵,当我们测量这个图象的紧密程度时约简在不确定增益.-This example of a main image used to calculate the information entropy, when we measured the extent of the image of the close of uncertainty when the gain reduction.
DecisionTree
- 通过构造决策树来进行分类,并用信息熵来剪枝获取最小的树从而进行属性约简-By constructing a decision tree for classification, and information entropy to obtain the smallest tree pruning in order to carry out attribute reduction
datareduct
- 基于香浓熵的属性简约，用于属性约简，以便分类-data reduction with fuzzy rough sets or fuzzy mutual information
SFG
- 基于信息熵计算属性的信息增益，最终得到约简后的属性集，实现特征选择-Feature Selection based on information entropy gain, then the reduct of attributes are obtained.
RoughSets(entropy-discritization)
- 基于信息熵的约简MATLAB代码，包含相关论文。-Based on information entropy reduction,matlab code, including related papers
粗糙集属性约简算法综述
- 属性约简是粗糙集理论中最核心的问题。文章阐述了基于信息熵、可辨识矩阵、遗传算法、 Johnson 等粗糙集属 性约简算法流程，指出了粗糙集属性约简算法的现有问题及发展趋势，促进粗糙集属性约简的研究进一步发展。(Attribute reduction is the most important problem in rough set theory. In this paper, the rough sets based on info
基于信息熵的约简MATLAB代码
- 利用matlab和ceemd进行编程求解简单的信息熵，根据求解的imf分量判断信号的情况(Using MATLAB and ceemd programming to solve simple information entropy, according to the IMF component to determine the situation of the signal)
带权重条件熵的属性约简算法
- 粗糙集理论中最重要的内容之一就是属性约简问题，现有的许多属性约简算法往往是基于属性对分类的重要性，如果属性约简的结果能满足用户实际需要的信息，如成本、用户的偏好等，那么约简理论将会有更高的实用价值。基于此，从信息熵的角度定义了带权重的属性重要性，然后重新定义了基于带权重的属性重要性的熵约简算法。最后通过实际例子说明，与基于属性重要性的熵约简算法相比，考虑权重的算法更加符合用户的实际需求。(Attribute reduction is o