1 citations · 1 across the 8 of their papers we have counts for
8 papers
Tracking changes using Kullback-Leibler divergence for the continual learning
Sebastián Basterrech, Michal Woźniak
Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of \emph{concept drift}, which consists of changing probabilistic ch…
Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification
Jędrzej Kozal, Michał Leś, Paweł Zyblewski +2
The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformatio…
Increasing Depth of Neural Networks for Life-long Learning
Jędrzej Kozal, Michał Woźniak
Purpose: We propose a novel method for continual learning based on the increasing depth of neural networks. This work explores whether extending neural network depth may be benefic…
Active Weighted Aging Ensemble for Drifted Data Stream Classification
Michał Woźniak, Paweł Zyblewski, Paweł Ksieniewicz
One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification…
Employing chunk size adaptation to overcome concept drift
Jędrzej Kozal, Filip Guzy, Michał Woźniak
Modern analytical systems must be ready to process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called conc…
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…
Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification
Joanna Grzyb, Jakub Klikowski, Michał Woźniak
The imbalanced data classification remains a vital problem. The key is to find such methods that classify both the minority and majority class correctly. The paper presents the cla…
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…