Available models

The following table summarises the supported hyperbox-based learning algorithms in this toolbox.

Model	Feature type	Model type	Learning type	Implementation	Example	References
EIOL-GFMM	Mixed	Single	Instance-incremental	ExtendedImprovedOnlineGFMM	Notebook	[1]
Freq-Cat-Onln-GFMM	Mixed	Single	Batch-incremental	FreqCatOnlineGFMM	Notebook	[2]
OneHot-Onln-GFMM	Mixed	Single	Batch-incremental	OneHotOnlineGFMM	Notebook	[2]
Onln-GFMM	Continuous	Single	Instance-incremental	OnlineGFMM	Notebook	[3], [4]
IOL-GFMM	Continuous	Single	Instance-incremental	ImprovedOnlineGFMM	Notebook	[5], [4]
FMNN	Continuous	Single	Instance-incremental	FMNNClassifier	Notebook	[6]
EFMNN	Continuous	Single	Instance-incremental	EFMNNClassifier	Notebook	[7]
KNEFMNN	Continuous	Single	Instance-incremental	KNEFMNNClassifier	Notebook	[8]
RFMNN	Continuous	Single	Instance-incremental	RFMNNClassifier	Notebook	[9]
AGGLO-SM	Continuous	Single	Batch	AgglomerativeLearningGFMM	Notebook	[10], [4]
AGGLO-2	Continuous	Single	Batch	AccelAgglomerativeLearningGFMM	Notebook	[10], [4]
MRHGRC	Continuous	Granularity	Multi-Granular learning	MultiGranularGFMM	Notebook	[11]
Decision-level Bagging of hyperbox-based learners	Continuous	Combination	Ensemble	DecisionCombinationBagging	Notebook	[12]
Decision-level Bagging of hyperbox-based learners with hyper-parameter optimisation	Continuous	Combination	Ensemble	DecisionCombinationCrossValBagging	Notebook
Model-level Bagging of hyperbox-based learners	Continuous	Combination	Ensemble	ModelCombinationBagging	Notebook	[12]
Model-level Bagging of hyperbox-based learners with hyper-parameter optimisation	Continuous	Combination	Ensemble	ModelCombinationCrossValBagging	Notebook
Random hyperboxes	Continuous	Combination	Ensemble	RandomHyperboxesClassifier	Notebook	[13]
Random hyperboxes with hyper-parameter optimisation for base learners	Continuous	Combination	Ensemble	CrossValRandomHyperboxesClassifier	Notebook

References

[1]

20. 20. Khuat and B. Gabrys “An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data”, ArXiv preprint, arXiv:2009.14670, 2020.

[2] (1,2)

20. 20. Khuat and B. Gabrys “An in-depth comparison of methods handling mixed-attribute data for general fuzzy min-max neural network”, Neurocomputing, vol 464, pp. 175-202, 2021.

[3]

2. Gabrys and A. Bargiela, “General fuzzy min-max neural network for clustering and classification”, IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 769-783, 2000.

[4] (1,2,3,4)

20. 20. Khuat and B. Gabrys, “Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule”, Information Sciences, vol. 547, pp. 887-909, 2021.

[5]

20. 20. Khuat, F. Chen, and B. Gabrys, “An improved online learning algorithm for general fuzzy min-max neural network”, in Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1-9, 2020.

[6]

16. Simpson, “Fuzzy min—max neural networks—Part 1: Classification”, IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 776-786, 1992.

[7]

13. Mohammed and C. P. Lim, “An enhanced fuzzy min-max neural network for pattern classification”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 3, pp. 417-429, 2014.

[8]

13. Mohammed and C. P. Lim, “Improving the Fuzzy Min-Max neural network with a k-nearest hyperbox expansion rule for pattern classification”, Applied Soft Computing, vol. 52, pp. 135-145, 2017.

[9]

15. 14. Al-Sayaydeh, M. F. Mohammed, E. Alhroob, H. Tao, and C. P. Lim, “A refined fuzzy min-max neural network with new learning procedures for pattern classification”, IEEE Transactions on Fuzzy Systems, vol. 28, no. 10, pp. 2480-2494, 2019.

[10] (1,2)

2. Gabrys, “Agglomerative learning algorithms for general fuzzy min-max neural network”, Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, vol. 32, no. 1, pp. 67-82, 2002.

[11]

T.T. Khuat, F. Chen, and B. Gabrys, “An Effective Multiresolution Hierarchical Granular Representation Based Classifier Using General Fuzzy Min-Max Neural Network”, IEEE Transactions on Fuzzy Systems, vol. 29, no. 2, pp. 427-441, 2021.

[12] (1,2)

2. Gabrys, “Combining neuro-fuzzy classifiers for improved generalisation and reliability”, in Proceedings of the 2002 International Joint Conference on Neural Networks, vol. 3, pp. 2410-2415, 2002.

[13]

20. 20. Khuat and B. Gabrys, “Random Hyperboxes”, IEEE Transactions on Neural Networks and Learning Systems, 2021.