Abstract
We apply rough set theory to obtain knowledge from the construction of a decision tree. Decision trees are widely used in machine learning. A variety of methods for making decision trees have been developed. Our algorithm, which compares the core attributes of objects and builds decision trees based on those attributes, represents a new type of tree construction. Experiments show that the new algorithm can help to extract more meaningful and accurate rules than other algorithms.
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Han, SW., Kim, JY. (2007). Rough Set-Based Decision Tree Construction Algorithm. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_58
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DOI: https://doi.org/10.1007/978-3-540-74472-6_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74468-9
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