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141 results on '"Ksieniewicz, Paweł"'

1. Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain

Author

Zyblewski, Paweł, Klikowski, Jakub, Borek-Marciniec, Weronika, and Ksieniewicz, Paweł

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it is assumed that deep learning methods are not the optimal solution for application in this field. However, excluding the entire -- and prevalent -- group of methods seems rather rash given the progress that has been made in recent years in its development. For this reason, the following paper is the first to present an approach to natural language data stream classification using the sentence space method, which allows for encoding text into the form of a discrete digital signal. This allows the use of convolutional deep networks dedicated to image classification to solve the task of recognizing fake news based on text data. Based on the real-life Fakeddit dataset, the proposed approach was compared with state-of-the-art algorithms for data stream classification based on generalization ability and time complexity., Comment: 8 pages, 8 figures

Published
2024

2. WarCov -- Large multilabel and multimodal dataset from social platform

Author

Borek-Marciniec, Weronika, Zyblewski, Pawel, Klikowski, Jakub, and Ksieniewicz, Pawel

Subjects
Computer Science - Computation and Language, Computer Science - Social and Information Networks
Abstract

In the classification tasks, from raw data acquisition to the curation of a dataset suitable for use in evaluating machine learning models, a series of steps - often associated with high costs - are necessary. In the case of Natural Language Processing, initial cleaning and conversion can be performed automatically, but obtaining labels still requires the rationalized input of human experts. As a result, even though many articles often state that "the world is filled with data", data scientists suffer from its shortage. It is crucial in the case of natural language applications, which is constantly evolving and must adapt to new concepts or events. For example, the topic of the COVID-19 pandemic and the vocabulary related to it would have been mostly unrecognizable before 2019. For this reason, creating new datasets, also in languages other than English, is still essential. This work presents a collection of 3~187~105 posts in Polish about the pandemic and the war in Ukraine published on popular social media platforms in 2022. The collection includes not only preprocessed texts but also images so it can be used also for multimodal recognition tasks. The labels define posts' topics and were created using hashtags accompanying the posts. The work presents the process of curating a dataset from acquisition to sample pattern recognition experiments., Comment: 13 pages, 6 figures

Published
2024

3. Unsupervised Concept Drift Detection based on Parallel Activations of Neural Network

Author

Komorniczak, Joanna and Ksieniewicz, Paweł

Subjects
Computer Science - Machine Learning
Abstract

Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or less chaotic degeneration - resulting directly in the concept drifts. The modern concept drift detectors almost always assume immediate access to labels, which due to their cost, limited availability and possible delay has been shown to be unrealistic. This work proposes an unsupervised Parallel Activations Drift Detector, utilizing the outputs of an untrained neural network, presenting its key design elements, intuitions about processing properties, and a pool of computer experiments demonstrating its competitiveness with state-of-the-art methods.

Published
2024

4. Taking Class Imbalance Into Account in Open Set Recognition Evaluation

Author

Komorniczak, Joanna and Ksieniewicz, Pawel

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from unknown classes and often induce an incorrect label with high confidence. Presented work looks at the evaluation of methods for Open Set Recognition, focusing on the impact of class imbalance, especially in the dichotomy between known and unknown samples. As an outcome of problem analysis, we present a set of guidelines for evaluation of methods in this field.

Published
2024

5. torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python

Author

Komorniczak, Joanna and Ksieniewicz, Pawel

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

The article presents the torchosr package - a Python package compatible with PyTorch library - offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for the Open Set Recognition task (where some classes are considered unknown and used only in the testing process) and additional tools to handle datasets and methods. The main goal of the package proposal is to simplify and promote the correct experimental evaluation, where experiments are carried out on a large number of derivative sets with various Openness and class-to-category assignments. The authors hope that state-of-the-art methods available in the package will become a source of a correct and open-source implementation of the relevant solutions in the domain.

Published
2023
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6. Involving Society to Protect Society from Fake News and Disinformation: Crowdsourced Datasets and Text Reliability Assessment

Author

Kątek, Gracjan, Gackowska, Marta, Komorniczak, Joanna, Ksieniewicz, Paweł, Kozik, Rafał, Pawlicki, Marek, Choraś, Michał, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Chbeir, Richard, editor, Manolopoulos, Yannis, editor, Fujita, Hamido, editor, Hong, Tzung-Pei, editor, Nguyen, Le Minh, editor, and Wojtkiewicz, Krystian, editor

Published
2024
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7. problexity -- an open-source Python library for binary classification problem complexity assessment

Author

Komorniczak, Joanna and Ksieniewicz, Pawel

Subjects
Computer Science - Machine Learning
Abstract

The classification problem's complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning -- becoming the basis for determining meta-attributes or multi-criteria optimization -- allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 complexity measures for the Python language -- compatible with the scikit-learn programming interface -- allowing for the implementation of research using them in the most popular programming environment of the machine learning community., Comment: 20 pages, 1 figure

Published
2022

8. Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification

Author

Kozal, Jędrzej, Leś, Michał, Zyblewski, Paweł, Ksieniewicz, Paweł, and Woźniak, Michał

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

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. Consequently, there is a need to develop novel fake news detection approaches capable of adapting to changing factual contexts and generalizing previously or concurrently acquired knowledge. To deal with this problem, we propose a lifelong learning-inspired approach, which allows for fake news detection in multiple languages and the mutual transfer of knowledge acquired in each of them. Both classical feature extractors, such as Term frequency-inverse document frequency or Latent Dirichlet Allocation, and integrated deep NLP (Natural Language Processing) BERT (Bidirectional Encoder Representations from Transformers) models paired with MLP (Multilayer Perceptron) classifier, were employed. The results of experiments conducted on two datasets dedicated to the fake news classification task (in English and Spanish, respectively), supported by statistical analysis, confirmed that utilization of additional languages could improve performance for traditional methods. Also, in some cases supplementing the deep learning method with classical ones can positively impact obtained results. The ability of models to generalize the knowledge acquired between the analyzed languages was also observed.

Published
2022

10. Analysis of the Possibility to Employ Relationship Between the Problem Complexity and the Classification Quality as Model Optimization Proxy

Author

Komorniczak, Joanna, Ksieniewicz, Paweł, Woźniak, Michał, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Burduk, Robert, editor, Choraś, Michał, editor, Kozik, Rafał, editor, Ksieniewicz, Paweł, editor, Marciniak, Tomasz, editor, and Trajdos, Paweł, editor

Published
2023
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11. Incremental Extreme Learning Machine for Binary Data Stream Classification

Author

Wojtachnia, Karol, Komorniczak, Joanna, Ksieniewicz, Paweł, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Burduk, Robert, editor, Choraś, Michał, editor, Kozik, Rafał, editor, Ksieniewicz, Paweł, editor, Marciniak, Tomasz, editor, and Trajdos, Paweł, editor

Published
2023
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12. Hollow n-grams Vectorizer for Natural Language Processing Problems

Author

Borek-Marciniec, Weronika, Ksieniewicz, Paweł, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Burduk, Robert, editor, Choraś, Michał, editor, Kozik, Rafał, editor, Ksieniewicz, Paweł, editor, Marciniak, Tomasz, editor, and Trajdos, Paweł, editor

Published
2023
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13. Active Weighted Aging Ensemble for Drifted Data Stream Classification

Author

Woźniak, Michał, Zyblewski, Paweł, and Ksieniewicz, Paweł

Subjects
Computer Science - Machine Learning
Abstract

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 task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon is required to adapt the classifier to the changing probabilistic characteristics. One of the significant problems in implementing such a solution is the access to data labels. It is usually costly, so to minimize the expenses related to this process, learning strategies based on semi-supervised learning are proposed, e.g., employing active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. This paper proposes a novel chunk-based method for non-stationary data streams based on classifier ensemble learning and an active learning strategy considering a limited budget that can be successfully applied to any data stream classification algorithm. The proposed method has been evaluated through computer experiments using both real and generated data streams. The results confirm the high quality of the proposed algorithm over state-of-the-art methods., Comment: 29 pages, 3 figures

Published
2021

16. SWAROG Project Approach to Fake News Detection Problem

Author

Kozik, Rafał, Komorniczak, Joanna, Ksieniewicz, Paweł, Pawlicka, Aleksandra, Pawlicki, Marek, Choraś, Michał, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Herrero, Álvaro, editor, Calvo Rolle, José Luis, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2023
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17. Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study

Author

Choras, Michal, Demestichas, Konstantinos, Gielczyk, Agata, Herrero, Alvaro, Ksieniewicz, Pawel, Remoundou, Konstantina, Urda, Daniel, and Wozniak, Michal

Subjects
Computer Science - Computation and Language, Computer Science - Social and Information Networks
Abstract

Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research.

Published
2020

18. stream-learn -- open-source Python library for difficult data stream batch analysis

Author

Ksieniewicz, Paweł and Zyblewski, Paweł

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incremental drift) in their recurring or non-recurring versions. The package allows conducting experiments following established evaluation methodologies (i.e. Test-Then-Train and Prequential). In addition, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-art chunk-based and online classifier ensembles. To improve computational efficiency, package utilises its own implementations of prediction metrics for imbalanced binary classification tasks.

Published
2020

22. Analysis of Variance Application in the Construction of Classifier Ensemble Based on Optimal Feature Subset for the Task of Supporting Glaucoma Diagnosis

Author

Sułot, Dominika, Zyblewski, Paweł, Ksieniewicz, Paweł, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Paszynski, Maciej, editor, Kranzlmüller, Dieter, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2021
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25. Combination of Active and Random Labeling Strategy in the Non-stationary Data Stream Classification

Author

Zyblewski, Paweł, Ksieniewicz, Paweł, Woźniak, Michał, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2020
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26. Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification

Author

Ksieniewicz, Paweł, Burduk, Robert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Krzhizhanovskaya, Valeria V., editor, Závodszky, Gábor, editor, Lees, Michael H., editor, Dongarra, Jack J., editor, Sloot, Peter M. A., editor, Brissos, Sérgio, editor, and Teixeira, João, editor

Published
2020
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27. Sentiment Analysis for Fake News Detection by Means of Neural Networks

Author

Kula, Sebastian, Choraś, Michał, Kozik, Rafał, Ksieniewicz, Paweł, Woźniak, Michał, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Krzhizhanovskaya, Valeria V., editor, Závodszky, Gábor, editor, Lees, Michael H., editor, Dongarra, Jack J., editor, Sloot, Peter M. A., editor, Brissos, Sérgio, editor, and Teixeira, João, editor

Published
2020
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28. Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification

Author

Ksieniewicz, Pawel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Krzhizhanovskaya, Valeria V., editor, Závodszky, Gábor, editor, Lees, Michael H., editor, Dongarra, Jack J., editor, Sloot, Peter M. A., editor, Brissos, Sérgio, editor, and Teixeira, João, editor

Published
2020
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29. Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks

Author

Ksieniewicz, Paweł, Goścień, Róża, Klinkowski, Mirosław, Walkowiak, Krzysztof, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Krzhizhanovskaya, Valeria V., editor, Závodszky, Gábor, editor, Lees, Michael H., editor, Dongarra, Jack J., editor, Sloot, Peter M. A., editor, Brissos, Sérgio, editor, and Teixeira, João, editor

Published
2020
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34. SMOTE Algorithm Variations in Balancing Data Streams

Author

Gulowaty, Bogdan, Ksieniewicz, Paweł, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Tallón-Ballesteros, Antonio J., editor, Menezes, Ronaldo, editor, and Allmendinger, Richard, editor

Published
2019
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35. A Genetic-Based Ensemble Learning Applied to Imbalanced Data Classification

Author

Klikowski, Jakub, Ksieniewicz, Paweł, Woźniak, Michał, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Tallón-Ballesteros, Antonio J., editor, Menezes, Ronaldo, editor, and Allmendinger, Richard, editor

Published
2019
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36. Machine Learning Methods for Fake News Classification

Author

Ksieniewicz, Paweł, Choraś, Michał, Kozik, Rafał, Woźniak, Michał, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Tallón-Ballesteros, Antonio J., editor, Menezes, Ronaldo, editor, and Allmendinger, Richard, editor

Published
2019
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37. Imbalance Reduction Techniques Applied to ECG Classification Problem

Author

Kozal, Jȩdrzej, Ksieniewicz, Paweł, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Tallón-Ballesteros, Antonio J., editor, Menezes, Ronaldo, editor, and Allmendinger, Richard, editor

Published
2019
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38. Combining Random Subspace Approach with smote Oversampling for Imbalanced Data Classification

Author

Ksieniewicz, Pawel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pérez García, Hilde, editor, Sánchez González, Lidia, editor, Castejón Limas, Manuel, editor, Quintián Pardo, Héctor, editor, and Corchado Rodríguez, Emilio, editor

Published
2019
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39. Classifier Selection for Highly Imbalanced Data Streams with Minority Driven Ensemble

Author

Zyblewski, Paweł, Ksieniewicz, Paweł, Woźniak, Michał, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2019
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41. Imbalanced Data Classification Based on Feature Selection Techniques

Author

Ksieniewicz, Paweł, Woźniak, Michał, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Yin, Hujun, editor, Camacho, David, editor, Novais, Paulo, editor, and Tallón-Ballesteros, Antonio J., editor

Published
2018
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42. Combined Classifier Based on Quantized Subspace Class Distribution

Author

Ksieniewicz, Paweł, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Yin, Hujun, editor, Camacho, David, editor, Novais, Paulo, editor, and Tallón-Ballesteros, Antonio J., editor

Published
2018
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45. A First Attempt to Construct Effective Concept Drift Detector Ensembles

Author

Woźniak, Michał, Ksieniewicz, Paweł, Kasprzak, Andrzej, Puchała, Karol, Ryba, Przemysław, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, and Choraś, Ryszard S., editor

Published
2017
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48. Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study

Author

Woźniak, Michał, Ksieniewicz, Paweł, Cyganek, Bogusław, Walkowiak, Krzysztof, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Saeed, Khalid, editor, and Homenda, Władysław, editor

Published
2016
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49. Ensemble of One-Dimensional Classifiers for Hyperspectral Image Analysis

Author

Ksieniewicz, Paweł, Krawczyk, Bartosz, Woźniak, Michał, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, and Shi, Yuhui, editor

Published
2016
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50. Active Learning Classifier for Streaming Data

Author

Woźniak, Michał, Cyganek, Bogusław, Kasprzak, Andrzej, Ksieniewicz, Paweł, Walkowiak, Krzysztof, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Martínez-Álvarez, Francisco, editor, Troncoso, Alicia, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2016
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