基于粗糙集和决策树的医学图像分类研究

根据医学图像数据的特性，提出一种基于粗糙集和决策树相结合的数据挖掘新方法。该方法利用粗糙集中基于属性重要性的离散化方法对医学图像特征进行离散化，采用粗糙集对其属性进行约简，得到低维训练数据，再用SLIQ决策树算法产生决策规则。实验表明：将粗糙理论与SLIQ相结合的数据挖掘方法既保留了原始数据的内部特点，同时剔除了与分类无关或关系不大的冗余特征，从而提高了分类的准确率和效率。

Research and application of medical image classification based on rough set theory and decision tree

According to the characteristic of medical image dataset,this paper introduces a new method of data mining based on rough set theory and decision tree,which applies the discretization method on the basis of the importance of attribute in rough set to discretize the feature and reduce attributes,then gets the low dimention training data,and finally makes use of the SLIQ algorithm of decision tree to extract classification rules.Lots of experiments prove that the new method of the combination of rough set theory and SLIQ algorithm applied in medical image classification improve recogniton rate and efficiency while reduce the irrelevant feature with classification and keep the inherent property of raw data.