Abstract
Rule extraction is an important issue in data mining field. In this paper, we study the extraction problem for the complete negative rules of the form ¬R →¬D. By integrating rough set theory and genetic algorithm, we propose a coverage matrix based on rough set to interpret the solution space and then transform the negative rule extraction into set cover problem which can be solved by genetic algorithm. We also develop a rule extraction system based on the existing data mining platform. Finally, we compare our approach with other related approaches in terms of F measure. The comparison experimental results on the real medical and benchmark datasets show that our approach performs efficiently for incompatible and value missing data.
This work is supported by the National Science Foundation of China under No. 60703111.
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Liu, J., Liu, Y., Long, Y. (2009). Integrating Rough Set and Genetic Algorithm for Negative Rule Extraction. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_7
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DOI: https://doi.org/10.1007/978-3-642-04394-9_7
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