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Andrzej Janusz
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2020 – today
- 2024
- [c58]Aleksandar M. Rakicevic, Pavle D. Milosevic, Ivana T. Dragovic, Ana M. Poledica, Milica M. Zukanovic, Andrzej Janusz, Dominik Slezak:
Predicting Stock Trends Using Common Financial Indicators: A Summary of FedCSIS 2024 Data Science Challenge Held on KnowledgePit.ai Platform. FedCSIS 2024: 731-737 - 2023
- [j15]Andrzej Janusz
, Andzelika Zalewska, Lukasz Wawrowski
BrightBox - A rough set based technology for diagnosing mistakes of machine learning models. Appl. Soft Comput. 141: 110285 (2023) - [j14]Andrzej Janusz
A practical study of methods for deriving insightful attribute importance rankings using decision bireducts. Inf. Sci. 645: 119354 (2023) - [c57]Mateusz Wnuk, Jan Dziuba, Andrzej Janusz
IEEE BigData Cup 2023 Report: Object Recognition with Muon Tomography Using Cosmic Rays. IEEE Big Data 2023: 6084-6091 - [c56]Daniel Kaluza
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations. ECAI 2023: 1207-1214 - [c55]Andrzej Janusz, Sebastian Stawicki:
Reducts in Rough Sets: Algorithmic Insights, Open Source Libraries and Applications (Tutorial - Extended Abstract). FedCSIS 2023: 71 - [c54]Michal Czerwinski, Marcin Michalak, Piotr Biczyk, Blazej Adamczyk, Daniel Iwanicki, Iwona Kostorz, Maksym Brzeczek, Andrzej Janusz
Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results. FedCSIS 2023: 1289-1293 - [c53]Andrzej Janusz
Predicting Frags in Tactic Games at KnowledgePit.ai: ICME 2023 Grand Challenge Report. ICME Workshops 2023: 1-5 - [c52]Marek Grzegorowski
Towards ML Explainability with Rough Sets, Clustering, and Dimensionality Reduction. IJCRS 2023: 371-386 - [c51]Daniel Kaluza
On Several New Dempster-Shafer-Inspired Uncertainty Measures Applicable for Active Learning. IJCRS 2023: 479-494 - [i5]Daniel Kaluza, Andrzej Janusz, Dominik Slezak:
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations. CoRR abs/2307.14380 (2023) - [i4]Andzelika Zalewska-Küpçü, Andrzej Janusz, Dominik Slezak:
Diagnostic Explainability by BrightBox. ERCIM News 2023(134) (2023) - 2022
- [j13]Andrzej Janusz
Learning multimodal entity representations and their ensembles, with applications in a data-driven advisory framework for video game players. Inf. Sci. 617: 193-210 (2022) - [c50]Marek Grzegorowski
Data-Driven Resilient Supply Management Supported by Demand Forecasting. ACIIDS (Companion) 2022: 122-134 - [c49]Daniel Kaluza, Andrzej Janusz
EVEAL - Expected Variance Estimation for Active Learning. IEEE Big Data 2022: 6222-6231 - [c48]Marcin S. Szczuka
IEEE BigData Cup 2022 Report Privacy-preserving Matching of Encrypted Images. IEEE Big Data 2022: 6471-6480 - [c47]Andrzej Janusz
KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions. FedCSIS 2022: 393-398 - [c46]Andrzej Janusz
Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results. FedCSIS 2022: 399-402 - [c45]Marek Grzegorowski
Prescriptive Analytics for Optimization of FMCG Delivery Plans. IPMU (2) 2022: 44-53 - 2021
- [j12]Marek Grzegorowski
Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning. Big Data Res. 25: 100203 (2021) - [c44]Maciej Matraszek, Andrzej Janusz
Predicting Victories in Video Games - IEEE BigData 2021 Cup Report. IEEE BigData 2021: 5664-5671 - [i3]Mateusz Przyborowski, Mateusz Pabis, Andrzej Janusz, Dominik Slezak:
Schema matching using Gaussian mixture models with Wasserstein distance. CoRR abs/2111.14244 (2021) - 2020
- [c43]Andrzej Janusz
Predicting Escalations in Customer Support: Analysis of Data Mining Challenge Results. IEEE BigData 2020: 5519-5526 - [c42]Andrzej Janusz
Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit. FedCSIS 2020: 77-80 - [c41]Joanna Henzel
On Positive-Correlation-Promoting Reducts. IJCRS 2020: 213-221
2010 – 2019
- 2019
- [c40]Andrzej Janusz
IEEE BigData 2019 Cup: Suspicious Network Event Recognition. IEEE BigData 2019: 5881-5887 - [c39]Andrzej Janusz
Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction. FedCSIS 2019: 3-6 - 2018
- [j11]Dominik Slezak
A framework for learning and embedding multi-sensor forecasting models into a decision support system: A case study of methane concentration in coal mines. Inf. Sci. 451-452: 112-133 (2018) - [c38]Mateusz Przyborowski, Tomasz Tajmajer, Lukasz Grad, Andrzej Janusz
Toward Machine Learning on Granulated Data - a Case of Compact Autoencoder-based Representations of Satellite Images. IEEE BigData 2018: 2657-2662 - [c37]Maciej Swiechowski
Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms. CIG 2018: 1-8 - [c36]Andrzej Janusz
Toward an Intelligent HS Deck Advisor: Lessons Learned from AAIA'18 Data Mining Competition. FedCSIS 2018: 189-192 - [c35]Andrzej Janusz
Utilizing Hybrid Information Sources to Learn Representations of Cards in Collectible Card Video Games. ICDM Workshops 2018: 422-429 - [c34]Andrzej Janusz
How to Match Jobs and Candidates - A Recruitment Support System Based on Feature Engineering and Advanced Analytics. IPMU (2) 2018: 503-514 - [c33]Andrzej Janusz
Investigating Similarity between Hearthstone Cards: Text Embeddings and Interchangeability Approaches. SMC 2018: 3421-3426 - [c32]Andrzej Janusz
SENSEI: An Intelligent Advisory System for the eSport Community and Casual Players. WI 2018: 754-757 - [i2]Maciej Swiechowski, Tomasz Tajmajer, Andrzej Janusz:
Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms. CoRR abs/1808.04794 (2018) - 2017
- [j10]Andrzej Janusz
Predicting seismic events in coal mines based on underground sensor measurements. Eng. Appl. Artif. Intell. 64: 83-94 (2017) - [j9]Sebastian Stawicki, Dominik Slezak, Andrzej Janusz, Sebastian Widz:
Decision bireducts and decision reducts - a comparison. Int. J. Approx. Reason. 84: 75-109 (2017) - [c31]Marek Grzegorowski
On the role of feature space granulation in feature selection processes. IEEE BigData 2017: 1806-1815 - [c30]Andrzej Janusz
Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge. FedCSIS 2017: 121-125 - [c29]Mathurin Aché, Andrzej Janusz
ISMIS 2017 Data Mining Competition: Trading Based on Recommendations. ISMIS 2017: 697-707 - [c28]Dominik Slezak
Toward Interactive Attribute Selection with Infolattices - A Position Paper. IJCRS (2) 2017: 526-539 - [i1]Andrzej Janusz, Maciej Swiechowski, Tomasz Tajmajer:
Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge. CoRR abs/1708.00730 (2017) - 2016
- [c27]Andrzej Janusz
Predicting Dangerous Seismic Events: AAIA'16 Data Mining Challenge. FedCSIS 2016: 205-211 - [e1]Víctor Flores, Fernando A. C. Gomide, Andrzej Janusz
Rough Sets - International Joint Conference, IJCRS 2016, Santiago de Chile, Chile, October 7-11, 2016, Proceedings. Lecture Notes in Computer Science 9920, 2016, ISBN 978-3-319-47159-4 [contents] - 2015
- [c26]Andrzej Janusz
Knowledge Pit - A Data Challenge Platform. CS&P 2015: 191-195 - [c25]Michal Meina, Andrzej Janusz
Tagging Firefighter Activities at the emergency scene: Summary of AAIA'15 data mining competition at knowledge pit. FedCSIS 2015: 367-373 - [c24]Andrzej Janusz
Computation of Approximate Reducts with Dynamically Adjusted Approximation Threshold. ISMIS 2015: 19-28 - [c23]Andrzej Janusz
Mining Data from Coal Mines: IJCRS'15 Data Challenge. RSFDGrC 2015: 429-438 - [c22]Andrzej Janusz
Rough Set Tools for Practical Data Exploration. RSKT 2015: 77-86 - 2014
- [j8]Andrzej Janusz
Rough Set Methods for Attribute Clustering and Selection. Appl. Artif. Intell. 28(3): 220-242 (2014) - [j7]Wojciech Swieboda, Adam Krasuski
Interactive Method for Semantic Document Indexing Based on Explicit Semantic Analysis. Fundam. Informaticae 132(3): 423-438 (2014) - [j6]Lala Septem Riza
Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "RoughSets". Inf. Sci. 287: 68-89 (2014) - [j5]Andrzej Janusz
Algorithms for Similarity Relation Learning from High Dimensional Data. Trans. Rough Sets 17: 174-292 (2014) - [c21]Lukasz Sosnowski
A Resemblance Based Approach for Recognition of Risks at a Fire Ground. AMT 2014: 559-570 - [c20]Andrzej Janusz
Adaptive Learning for Improving Semantic Tagging of Scientific Articles. FedCSIS 2014: 27-34 - [c19]Andrzej Janusz
Key Risk Factors for Polish State Fire Service: a Data Mining Competition at Knowledge Pit. FedCSIS 2014: 345-354 - [c18]Andrzej Janusz
Assessment of data granulations in context of feature extraction problem. GrC 2014: 116-120 - [c17]Andrzej Janusz
Random Probes in Computation and Assessment of Approximate Reducts. RSEISP 2014: 53-64 - 2013
- [j4]Marcin S. Szczuka, Andrzej Janusz
Semantic Clustering of Scientific Articles Using Explicit Semantic Analysis. Trans. Rough Sets 16: 83-102 (2013) - [c16]Adam Krasuski, Andrzej Janusz:
Semantic Tagging of Heterogeneous Data: Labeling Fire & Rescue Incidents with Threats. FedCSIS 2013: 77-82 - [c15]Andrzej Janusz
Improving Semantic Clustering of EWID Reports by Using Heterogeneous Data Types. RSFDGrC 2013: 304-314 - [p2]Karol Kurach, Krzysztof Pawlowski, Lukasz Romaszko, Marcin Tatjewski, Andrzej Janusz
Multi-label Classification of Biomedical Articles. Intelligent Tools for Building a Scientific Information Platform 2013: 199-214 - 2012
- [j3]Andrzej Janusz
Unsupervised Similarity Learning from Textual Data. Fundam. Informaticae 119(3-4): 319-336 (2012) - [j2]Andrzej Janusz
Combining multiple predictive models using genetic algorithms. Intell. Data Anal. 16(5): 763-776 (2012) - [j1]Andrzej Janusz
Dynamic Rule-Based Similarity Model for DNA Microarray Data. Trans. Rough Sets 15: 1-25 (2012) - [c14]Karol Kurach, Krzysztof Pawlowski, Lukasz Romaszko, Marcin Tatjewski, Andrzej Janusz
An Ensemble Approach to Multi-label Classification of Textual Data. ADMA 2012: 306-317 - [c13]Andrzej Janusz, Dominik Slezak:
Utilization of Attribute Clustering Methods for Scalable Computation of Reducts from High-Dimensional Data. FedCSIS 2012: 295-302 - [c12]Andrzej Janusz
Interactive Document Indexing Method Based on Explicit Semantic Analysis. RSCTC 2012: 156-165 - [c11]Andrzej Janusz
JRS'2012 Data Mining Competition: Topical Classification of Biomedical Research Papers. RSCTC 2012: 422-431 - [p1]Marcin S. Szczuka
Semantic Clustering of Scientific Articles with Use of DBpedia Knowledge Base. Intelligent Tools for Building a Scientific Information Platform 2012: 61-76 - 2011
- [c10]Dominik Slezak, Andrzej Janusz
Ensembles of Bireducts: Towards Robust Classification and Simple Representation. FGIT 2011: 64-77 - [c9]Andrzej Janusz
Applications of Approximate Reducts to the Feature Selection Problem. RSKT 2011: 45-50 - [c8]Marcin S. Szczuka
Clustering of Rough Set Related Documents with Use of Knowledge from DBpedia. RSKT 2011: 394-403 - [c7]Dominik Slezak, Andrzej Janusz
Semantic Analytics of PubMed Content. USAB 2011: 63-74 - 2010
- [c6]Andrzej Janusz
Utilization of Dynamic Reducts to Improve Performance of the Rule-Based Similarity Model for Highly-Dimensional Data. Web Intelligence/IAT Workshops 2010: 432-435 - [c5]Andrzej Janusz
Discovering Rules-Based Similarity in Microarray Data. IPMU 2010: 49-58 - [c4]Marcin Wojnarski, Andrzej Janusz
RSCTC'2010 Discovery Challenge: Mining DNA Microarray Data for Medical Diagnosis and Treatment. RSCTC 2010: 4-19 - [c3]Andrzej Janusz
Combining Multiple Classification or Regression Models Using Genetic Algorithms. RSCTC 2010: 130-137
2000 – 2009
- 2009
- [c2]Andrzej Janusz
Rule-Based Similarity for Classification. Web Intelligence/IAT Workshops 2009: 449-452 - 2008
- [c1]Andrzej Janusz
Similarity Relation in Classification Problems. RSCTC 2008: 211-222
Coauthor Index
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last updated on 2024-12-05 21:39 CET by the dblp team
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