Free Neuro-Fuzzy Library

Neuro-fuzzy is an implementation of fuzzy tools for data analysis:

• data modifiers,
• clustering algorithms,
• fuzzy systems,
• neuro-fuzzy systems.

The project is implemented in the C++ language.

There are 5 examples of the usage of the library.

Run make 1 to run the 1st example, make 2 – the 2nd example, and finally make 5 to run the 5th example.

Run make doxygen to produce a Doxygen documentation and UML class diagrams for the project.

Citation request If you use this library, please cite ¹

Examples

make 1

Run make 1 for this example.

This example presents modifiers of data:

1. normalisation
2. standardisation
3. marginalisation of incomplete data items
4. imputation of missing data with a constant
5. imputation of missing data with averages of the attributes
6. imputation of missing data with medians of the attributes
7. imputation of missing data with averages of 3 nearest neighbours
8. imputation of missing data with medians of 3 nearest neighbours
9. imputation of missing data with values from 3 nearest neighbours and then the imputed data set standardized
10. a series of data modifiers: imputer with values from knn (k == 3) + standardiser, imputer with constant value, normaliser, standardiser, average imputer, imputer with constant value + normaliser, average imputer + standardiser
11. imputer with constant value + normaliser + average imputer + standardiser
12. split of a data set into n chunks of train and test datasets for n-fold cross-validation
13. split of a data set into n chunks of train, validate, and test datasets for n-fold cross-validation

make 2

Run make 2 for this example.

This example presents clustering algorithms:

1. FCM (fuzzy c-means)
2. FCM with Euclidean metric
3. FCM with cosine metric
4. FCM with Chebyshev metric
5. FCM with Minkowski metric
6. FCM with Manhattan metric
7. possibilistic fcm
8. conditional fcm
9. Gustafson-Kessel
10. subspace fcm (Citation request If you use this algorithm, please cite ² and ¹)
11. fubi (fuzzy biclustering) (Citation request If you use this algorithm, please cite ³ and ¹)
12. fcom
13. dbscan (Citation request If you use this algorithm, please cite ⁴)
14. grdbscan (granular dbscan) (Citation request If you use this algorithm, please cite ⁴)

make 3

Run make 3 for this example.

This example presents clustering algorithms for incomplete datasets:

1. rfcm (Citation request If you use this algorithm, please cite ⁵ and ¹)
2. rfcom (Citation request If you use this algorithm, please cite ⁶ and ¹)
3. rsfcm (Citation request If you use this algorithm, please cite ⁷ and ¹)

make 4

Run make 4 for this example.

This example presents a fuzzy system.

make 5

Run make 5 for this example.

This example presents neuro-fuzzy systems for

1. classification
   1. MA
   2. TSK
   3. ANNBFIS
   4. subspace ANNBFIS (Citation request If you use this algorithm, please cite ² and ¹)
   5. FuBi ANNBFIS (Citation request If you use this algorithm, please cite ³ and ¹)
   6. 3WDNFS: three way decision neuro-fuzzy system (Citation request If you use this algorithm, please cite ⁸ and ¹)
   7. Minkowski prototype TSK
   8. Minkowski prototype ANNBFIS
   9. Mahalanobis prototype TSK
   10. Mahalanobis prototype ANNBFIS
2. regression:
   1. MA
   2. TSK
   3. ANNBFIS
   4. subspace ANNBFIS (Citation request If you use this algorithm, please cite ² and ¹)
   5. FuBi ANNBFIS (Citation request If you use this algorithm, please cite ³ and ¹)
   6. Minkowski prototype TSK (Citation request If you use this algorithm, please cite ⁹ and ¹)
   7. Minkowski prototype ANNBFIS (Citation request If you use this algorithm, please cite ⁹ and ¹)
   8. Mahalanobis prototype TSK
   9. Mahalanobis prototype ANNBFIS

Models for the MA, TSK, and ANNBFIS systems are saved into files in numeric and liguistic description ¹⁰.

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Footnotes

1. Krzysztof Siminski, NFL -- Free Library for Fuzzy and Neuro-Fuzzy Systems, [in] Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis (Stanisław Kozielski, Dariusz Mrozek, Paweł Kasprowski, Bożena Małysiak-Mrozek, Daniel Kostrzewa, eds.), Springer International Publishing, 2019, pp. 139-150.

   @inproceedings{id:Siminski2019NFL,
   	address = "Cham",
   	author = "Krzysztof Siminski",
   	booktitle = "Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis",
   	doi = "10.1007/978-3-030-19093-4_11",
   	editor = "Stanisław Kozielski and Dariusz Mrozek and Paweł Kasprowski and Bożena Małysiak-Mrozek and Daniel Kostrzewa",
   	isbn = "978-3-030-19093-4",
   	pages = "139–150",
   	publisher = "Springer International Publishing",
   	title = "{NFL} – Free Library for Fuzzy and Neuro-Fuzzy Systems",
   	year = "2019"
   }
   

   ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³

2. Krzysztof Siminski, Neuro-fuzzy system with weighted attributes, [in] Soft Computing, 2014, volume 18, number 2, pp. 285-297. doi: 10.1007/s00500-013-1057-z

   ARTICLE{id:Siminski2014Neuro,
         author = {Krzysztof Siminski},
         title = {Neuro-fuzzy system with weighted attributes},
         journal = {Soft Computing},
         volume = {18}, 
         number = {2}, 
         pages = {285--297}, 
         year = {2014},
         doi = {10.1007/s00500-013-1057-z},
   }
   

   ↩ ↩² ↩³

3. Krzysztof Siminski, FuBiNFS – fuzzy biclustering neuro-fuzzy system, [in] Fuzzy Sets and Systems, 2022, volume 438, pp. 84–106

   @article{id:Siminski2022FuBiNFS,
   	author = "Krzysztof Siminski",
   	doi = "10.1016/j.fss.2021.07.009",
   	issn = "0165-0114",
   	journal = "Fuzzy Sets and Systems",
   	keywords = "Neuro-fuzzy system; Biclustering; Subspace clustering; Subspace neuro-fuzzy system; Attribute weights",
   	pages = "84–106",
   	title = "FuBiNFS – fuzzy biclustering neuro-fuzzy system",
   	url = "https://www.sciencedirect.com/science/article/pii/S0165011421002499",
   	volume = "438",
   	year = "2022"
   }
   

   ↩ ↩² ↩³

4. Dawid Suchy, Krzysztof Siminski, GrDBSCAN: A granular density–based clustering algorithm, [in] International Journal of Applied Mathematics and Computer Science, 2023, volume 33, number 2, pp. 297–312.

   @Article{id:Suchy2023GrDBSCAN,
   	author   = {Dawid Suchy and Krzysztof Siminski},
   	journal  = {International Journal of Applied Mathematics and Computer Science},
   	title    = {GrDBSCAN: A granular density–based clustering algorithm},
   	year     = {2023},
   	pages    = {297–312},
   	volume   = {33},
   	number   = {2},
   	doi      = {10.34768/amcs-2023-0022},
   }
   

   ↩ ↩²

5. Krzysztof Simiński, Clustering with missing values, [in] Fundamenta Informaticae, 2013, volume 123, number 3, pp. 331-350.

   @ARTICLE{id:Siminski2013Clustering,
         author = {Krzysztof Siminski},
         title = {Clustering with missing values},
         journal = {Fundamenta Informaticae},
         year = {2013},
         volume = {123},
         pages = {331-350},
         number = {3},
   }
   

   ↩

6. Krzysztof Siminski, Robust subspace neuro-fuzzy system with data ordering, [in] Neurocomputing, 2017, volume 238, pp. 33-43

   @article{id:Siminski2017Robust,
   	year = {2017},
   	author = {Krzysztof Siminski},
   	journal = {Neurocomputing},
   	pages = {33-43},
   	volume = {238},
   	title = {Robust subspace neuro-fuzzy system with data ordering},
   	doi = {10.1016/j.neucom.2017.01.034},
   }
   

   ↩

7. Krzysztof Simiński, Rough fuzzy subspace clustering for data with missing values, Computing & Informatics, 2014, volume 33, number 1, pp. 131-153.

   @article{id:Siminski2014Rough,
     title={Rough fuzzy subspace clustering for data with missing values},
     author={Siminski, Krzysztof},
     journal={Computing \& Informatics},
     volume={33},
     number={1},
     year={2014},
     pages={131--153},
   } 
   

   ↩

8. Krzysztof Siminski, 3WDNFS – Three-way decision neuro-fuzzy system for classification, [in] Fuzzy Sets and Systems, 2023, volume 466, pp. 108432

   @article{id:Siminski20233WDNFS,
   	author   = {Krzysztof Siminski},
   	title    = {{3WDNFS} – Three-way decision neuro-fuzzy system for classification},
   	journal  = {Fuzzy Sets and Systems},
   	volume   = {466},
   	pages    = {108432},
   	year     = {2023},
   	issn     = {0165-0114},
   	doi      = {10.1016/j.fss.2014.12.007},
   }
   

   ↩

9. Krzysztof Siminski, Prototype based granular neuro-fuzzy system for regression task, [in] Fuzzy Sets and Systems, 2022, volume 449, pp. 56-78.

   @article{id:Siminski2022Prototype,
   	title = {Prototype based granular neuro-fuzzy system for regression task},
   	journal = {Fuzzy Sets and Systems},
   	volume = {449},
   	pages = {56-78},
   	year = {2022}, 
   	issn = {0165-0114},
   	doi = {10.1016/j.fss.2022.03.001},
   	author = {Krzysztof Siminski},
   	url = {https://www.sciencedirect.com/science/article/pii/S0165011422000963},
   	keywords = {Prototype, Granular computing, Neuro-fuzzy system, Regression},
   }
   

   ↩ ↩²

10. Krzysztof Siminski, Konrad Wnuk, Automatic Extraction of Linguistic Description from Fuzzy Rule Base, 2024

    @misc{id:Siminski2024Automatic,
      title={Automatic Extraction of Linguistic Description from Fuzzy Rule Base}, 
      author={Krzysztof Siminski and Konrad Wnuk},
      year={2024},
      eprint={2404.03058},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
    }
    

    ↩