论文标题
用于特征选择，分类和知识表示的高效，准确的粗糙集
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
论文作者
论文摘要
本文提出了一种基于粗糙集的强大数据挖掘方法，该方法可以同时实现特征选择，分类和知识表示。粗糙集具有良好的解释性，并且是特征选择的流行方法。但是，低效率和低精度是限制其应用能力的主要缺点。在与准确性相对应的本文中，我们首先发现由于过度拟合，尤其是在处理噪声属性时，粗糙集的无效性，并提出了对属性的鲁棒测量，称为相对重要性。我们提出了知识表征和分类的“粗糙概念树”的概念。公共基准数据集的实验结果表明，所提出的框架的准确性高于七个流行或最先进的特征选择方法。
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In this paper,corresponding to the accuracy, we first find the ineffectiveness of rough set because of overfitting, especially in processing noise attribute, and propose a robust measurement for an attribute, called relative importance.we proposed the concept of "rough concept tree" for knowledge representation and classification. Experimental results on public benchmark data sets show that the proposed framework achieves higher accurcy than seven popular or the state-of-the-art feature selection methods.
