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 particl

联合运用粗糙集(RS)理论-遗传算法(GA)-支持向量机(SVM)方法研究真核生物翻译起始位点(TIS)的识别.-A classification model is built to recognize translation initiation sites (TISs) in eukaryotes by applying rough sets-genetic algorithm-support vector machine (RS-GA-SVM).

基于粗糙集的图像语义自动标注分类算法代码-Image semantic auto-tagging based on rough set classification algorithm code