Abstract
Decision trees are widely used in data mining and machine learning for classification. In the process of constructing a tree, the criteria of selecting partitional attributes will influence the classification accuracy of the tree. In this paper, we present a new concept, weighted mean roughness, which is based on rough set theory, for choosing attributes. The experimental result shows that compared with the entropy-based approach, our approach is a better way to select nodes for constructing decision trees.
This paper is supported by National Science Foundation of China No. 60373108 and Network Computing Key Specialities of Northwest Normal University.
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Yun, J., Zhanhuai, L., Yang, Z., Qiang, Z. (2004). A New Approach for Selecting Attributes Based on Rough Set Theory. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_22
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DOI: https://doi.org/10.1007/978-3-540-28651-6_22
Publisher Name: Springer, Berlin, Heidelberg
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