4 citations · 5 across the 2 of their papers we have counts for
8 papers · 1 filter
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Piotr Gaiński, Michał Koziarski, Jacek Tabor +1
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality…
Imbalanced data preprocessing techniques utilizing local data characteristics
Michał Koziarski
Data imbalance, that is the disproportion between the number of training observations coming from different classes, remains one of the most significant challenges affecting contem…
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…