An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
This work addresses the problem of improving rough set methods for data mining tasks, offering incremental enhancements in accuracy and efficiency for applications in feature selection and classification.
The paper tackled the low efficiency and accuracy of rough set methods for feature selection, classification, and knowledge representation by introducing a robust measurement called relative importance and a rough concept tree, achieving higher accuracy than seven state-of-the-art methods on benchmark datasets.
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.