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

TL;DR

This paper introduces a novel RRC model using truncated normal distribution instead of beta, demonstrating comparable or improved performance in classifying minority classes across various classifiers.

Contribution

It proposes a new RRC model with truncated normal distribution and heuristic methods, expanding the probabilistic tools for classifier combination.

Findings

Comparable performance to beta-based RRC model

Improved minority class detection for some classifiers

Effective use of truncated normal distribution in ensemble methods

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.