An Empirical Comparison of Rule Sets Induced by LERS and Probabilistic Rough Classification
- Cite this paper as:
- Grzymala-Busse J.W., Marepally S.R., Yao Y. (2010) An Empirical Comparison of Rule Sets Induced by LERS and Probabilistic Rough Classification. In: Szczuka M., Kryszkiewicz M., Ramanna S., Jensen R., Hu Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science, vol 6086. Springer, Berlin, Heidelberg
In this paper we present results of an experimental comparison (in terms of an error rate) of rule sets induced by the LERS data mining system with rule sets induced using the probabilistic rough classification (PRC). As follows from our experiments, the performance of LERS (possible rules) is significantly better than the best rule sets induced by PRC with any threshold (two-tailed test, 5% significance level). Additionally, the LERS possible rule approach to rule induction is significantly better than the LERS certain rule approach (two-tailed test, 5% significance level).
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