no code implementations • 11 Apr 2024 • Szymon Wojciechowski, Michał Woźniak
Although it is pretty common to have metrics indicating the classification quality within each class, for the end user, the analysis of several such metrics is then required, which in practice causes difficulty in interpreting the usefulness of a given classifier.
2 code implementations • 5 Apr 2024 • Jędrzej Kozal, Jan Wasilewski, Bartosz Krawczyk, Michał Woźniak
Continual learning poses a fundamental challenge for modern machine learning systems, requiring models to adapt to new tasks while retaining knowledge from previous ones.
1 code implementation • 7 Sep 2023 • Radek Svoboda, Sebastian Basterrech, Jędrzej Kozal, Jan Platoš, Michał Woźniak
Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by industrial entities.
no code implementations • 30 Apr 2023 • Michał Leś, Michał Woźniak
Achieved results prove that using synthesized data for training may be a good base for pretraining general-purpose models, where the task of transcription is not focused on one instrument.
no code implementations • 28 Mar 2023 • Jan Idziak, Artjoms Šeļa, Michał Woźniak, Albert Leśniak, Joanna Byszuk, Maciej Eder
Our study provides a working solution that reads the cards, and links their lemmas to a searchable list of dictionary entries, for a large historical dictionary entitled the Dictionary of the 17th- and 18th-century Polish, which comprizes 2. 8 million index cards.
no code implementations • 11 Jan 2023 • Jędrzej Kozal, Michał Woźniak
We propose a new method that builds an ensemble classifier.
no code implementations • 25 May 2022 • Jędrzej Kozal, Michał Leś, Paweł Zyblewski, Paweł Ksieniewicz, Michał Woźniak
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 disinformation on a larger scale than ever before.
no code implementations • 22 Feb 2022 • Jędrzej Kozal, Michał Woźniak
Purpose: We propose a novel method for continual learning based on the increasing depth of neural networks.
no code implementations • 25 Oct 2021 • Jędrzej Kozal, Filip Guzy, Michał Woźniak
Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous adaptation of the model to the changing data distributions.
1 code implementation • 9 May 2021 • Michał Koziarski, Colin Bellinger, Michał Woźniak
Our $5\times2$ cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.
no code implementations • 30 Jan 2021 • Joanna Grzyb, Jakub Klikowski, Michał Woźniak
The paper includes an experimental evaluation of the method based on the conducted experiments.
no code implementations • 7 Apr 2020 • Michał Koziarski, Michał Woźniak, Bartosz Krawczyk
The proposed method utilizes an energy-based approach to modeling the regions suitable for oversampling, less affected by small disjuncts and outliers than SMOTE.
no code implementations • 17 Nov 2018 • José-Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michał Woźniak, Salvador García
Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making.