activity
20192021
most citedCombination of linear classifiers using score function -- analysis of possible combination strategies

7 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Building an Ensemble of Classifiers via Randomized Models of Ensemble Members

Pawel Trajdos, Marek Kurzynski

Many dynamic ensemble selection (DES) methods are known in the literature. A previously-developed by the authors, method consists in building a randomized classifier which is treat…

cs.LG2021

Soft Confusion Matrix Classifier for Stream Classification

Pawel Trajdos, Marek Kurzynski

In this paper, the issue of tailoring the soft confusion matrix (SCM) based classifier to deal with stream learning task is addressed. The main goal of the work is to develop a wra…

cs.LG20206 cited

Linear Classifier Combination via Multiple Potential Functions

Pawel Trajdos, Robert Burduk

A vital aspect of the classification based model construction process is the calibration of the scoring function. One of the weaknesses of the calibration process is that it does n…

cs.LG20191 cited

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

Pawel Trajdos, Marek Kurzynski

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…

cs.LG20197 cited

Combination of linear classifiers using score function -- analysis of possible combination strategies

Pawel Trajdos, Robert Burduk

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision bounda…

cs.LG20191 cited

Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification

Pawel Trajdos, Marcin Majak

The aim of this paper was to compare soft confusion matrix approach and Bayes metaclassifier under the multi-label classification framework. Although the methods were successfully…