1 citations · 1 across the 1 of their papers we have counts for
6 papers
RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
Michał Koziarski, Colin Bellinger, Michał Woźniak
Real-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such ca…
Potential Anchoring for imbalanced data classification
Michał Koziarski
Data imbalance remains one of the factors negatively affecting the performance of contemporary machine learning algorithms. One of the most common approaches to reducing the negati…
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise
Michał Koziarski, Michał Woźniak, Bartosz Krawczyk
The imbalanced data classification is one of the most crucial tasks facing modern data analysis. Especially when combined with other difficulty factors, such as the presence of noi…
CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification
Michał Koziarski
In this paper we propose a novel data-level algorithm for handling data imbalance in the classification task, Synthetic Majority Undersampling Technique (SMUTE). SMUTE leverages th…
Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification
Michał Koziarski
Data imbalance remains one of the open challenges in the contemporary machine learning. It is especially prevalent in case of medical data, such as histopathological images. Tradit…
Radial-Based Undersampling for Imbalanced Data Classification
Michał Koziarski
Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorith…