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Found 10 papers, 5 papers with code
Building an Ensemble of Classifiers via Randomized Models of Ensemble Members
In this paper, a novel randomized model of base classifier is developed.
Soft Confusion Matrix Classifier for Stream Classification
The second one is aimed at effective dealing with the concept drift.
Probability-driven scoring functions in combining linear classifiers
This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers.
Linear Classifier Combination via Multiple Potential Functions
A vital aspect of the classification based model construction process is the calibration of the scoring function.
Combination of linear classifiers using score function -- analysis of possible combination strategies
In this work, we addressed the issue of combining linear classifiers using their score functions.
Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers
More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution.
Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification
The aim of this paper was to compare soft confusion matrix approach and Bayes metaclassifier under the multi-label classification framework.
Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix
The classifier weights promote base classifiers which are the most susceptible to the correction based on the fuzzy confusion matrix.
A Correction Method of a Binary Classifier Applied to Multi-label Pairwise Models
In this work, we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification.
Dynamic classifier chains for multi-label learning
The experimental results showed that the proposed model based on Naive Bayes classifier the above-mentioned heuristic is an efficient tool for building dynamic chain classifiers.
