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39 results on '"Siminski, Krzysztof"'

1. Automatic Extraction of Linguistic Description from Fuzzy Rule Base

Author

Siminski, Krzysztof and Wnuk, Konrad

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Neuro-fuzzy systems are a technique of explainable artificial intelligence (XAI). They elaborate knowledge models as a set of fuzzy rules. Fuzzy sets are crucial components of fuzzy rules. They are used to model linguistic terms. In this paper, we present an automatic extraction of fuzzy rules in the natural English language. Full implementation is available free from a public repository.

Published
2024

2. GrDBSCAN: A Granular Density–Based Clustering Algorithm

Author

Suchy Dawid and Siminski Krzysztof

Subjects
granular computing, dbscan, clustering, grdbscan, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback—its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.

Published
2023
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6. GrFCM – Granular Clustering of Granular Data

Author

Siminski, Krzysztof, 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, Gruca, Aleksandra, editor, Czachórski, Tadeusz, editor, Deorowicz, Sebastian, editor, Harężlak, Katarzyna, editor, and Piotrowska, Agnieszka, editor

Published
2020
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7. FIT2COMIn – Robust Clustering Algorithm for Incomplete Data

Author

Siminski, Krzysztof, 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, Gruca, Aleksandra, editor, Czachórski, Tadeusz, editor, Deorowicz, Sebastian, editor, Harężlak, Katarzyna, editor, and Piotrowska, Agnieszka, editor

Published
2020
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8. GrNFS: A Granular Neuro–Fuzzy System for Regression in Large Volume Data

Author

Siminski Krzysztof

Subjects
granular computing, neuro-fuzzy system, large volume data, machine learning, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

Neuro-fuzzy systems have proved their ability to elaborate intelligible nonlinear models for presented data. However, their bottleneck is the volume of data. They have to read all data in order to produce a model. We apply the granular approach and propose a granular neuro-fuzzy system for large volume data. In our method the data are read by parts and granulated. In the next stage the fuzzy model is produced not on data but on granules. In the paper we introduce a novel type of granules: a fuzzy rule. In our system granules are represented by both regular data items and fuzzy rules. Fuzzy rules are a kind of data summaries. The experiments show that the proposed granular neuro-fuzzy system can produce intelligible models even for large volume datasets. The system outperforms the sampling techniques for large volume datasets.

Published
2021
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9. An outlier–robust neuro–fuzzy system for classification and regression

Author

Siminski Krzysztof

Subjects
outliers, neuro-fuzzy systems, clustering, classification, regression, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.

Published
2021
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10. NFL – Free Library for Fuzzy and Neuro-Fuzzy Systems

Author

Siminski, Krzysztof, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Kozielski, Stanisław, editor, Mrozek, Dariusz, editor, Kasprowski, Paweł, editor, Małysiak-Mrozek, Bożena, editor, and Kostrzewa, Daniel, editor

Published
2019
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11. Improvement of Precision of Neuro-Fuzzy System by Increase of Activation of Rules

Author

Siminski, Krzysztof, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Kozielski, Stanisław, editor, Mrozek, Dariusz, editor, Kasprowski, Paweł, editor, Małysiak-Mrozek, Bożena, editor, and Kostrzewa, Daniel, editor

Published
2016
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12. Memetic Neuro-Fuzzy System with Differential Optimisation

Author

Siminski, Krzysztof, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Kozielski, Stanisław, editor, Mrozek, Dariusz, editor, Kasprowski, Paweł, editor, Małysiak-Mrozek, Bożena, editor, and Kostrzewa, Daniel, editor

Published
2016
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13. New Rough-Neuro-Fuzzy Approach for Regression Task in Incomplete Data

Author

Siminski, Krzysztof, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Kozielski, Stanisław, editor, Mrozek, Dariusz, editor, Kasprowski, Paweł, editor, Małysiak-Mrozek, Bożena, editor, and Kostrzewa, Daniel, editor

Published
2016
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28. Simplification of Neuro-Fuzzy Models

Author

Simiński, Krzysztof, Kacprzyk, Janusz, editor, Cyran, Krzysztof A., editor, Kozielski, Stanisław, editor, Peters, James F., editor, Stańczyk, Urszula, editor, and Wakulicz-Deja, Alicja, editor

Published
2009
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31. The Deep Parser for Polish

Author

Suszczańska, Nina, Szmal, Przemysław, Simiński, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Vetulani, Zygmunt, editor, and Uszkoreit, Hans, editor

Published
2009
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33. Comparison of Algorithms for Clustering Incomplete Data

Author

Matyja Artur and Siminski Krzysztof

Subjects
clustering, incomplete data, missing value, marginalisation, imputation, ifcm, ocs, nps, ncs, Electronic computers. Computer science, QA75.5-76.95
Abstract

The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets.

Published
2014
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39. COMPARISON OF INCOMPLETE DATA HANDLING TECHNIQUES FOR NEURO-FUZZY SYSTEMS.

Author

SIKORA, MARCIN and SIMINSKI, KRZYSZTOF

Subjects
BIG data, FUZZY systems, COMPUTER algorithms, COMPUTER systems, COMPUTER science research
Abstract

Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering. [ABSTRACT FROM AUTHOR]

Published
2014
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