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

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 977)

Abstract

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

Keywords

Classification Randomized reference classifier Gaussian distribution Multiclassifier systems 

Notes

Acknowledgments

This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wroclaw University of Science and TechnologyWroclawPoland

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