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In this paper, an issue of building the RRC model using probabilitydistributions other than beta distribution is addressed. More precisely, inthis paper, we propose to build the RRR model using the truncated normaldistribution. Heuristic procedures for expected value and the variance of thetruncated-normal distribution are also proposed. The proposed approach istested using SCM-based model for testing the consequences of applying thetruncated normal distribution in the RRC model. The experimental evaluation isperformed using four different base classifiers and seven quality measures. Theresults showed that the proposed approach is comparable to the RRC model builtusing beta distribution. What is more, for some base classifiers, thetruncated-normal-based SCM algorithm turned out to be better at discoveringobjects coming from minority classes.

Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers