A stacked architecture-based fuzzy classifier with data position transformation using fuzzy cognitive maps
Pages 2037 - 2052
Abstract
The amalgamation of fuzzy model and deep learning has become one hot topic in today’s fuzzy community. However, with the model goes deeper, a pivotal aspect for performance enhancement, the interpretability of the model will deteriorate. To enhance the classification accuracy of classifiers while ensuring interpretability, we propose a stacked architecture-based fuzzy classifier named PT-SAFC. Borrowing the hierarchically stacked thought originated from deep learning, the PT-SAFC is composed by stacking two distinct fuzzy systems, implemented by fuzzy neuro-networks. Here, we propose an improved Takagi-Sugeno-Kang (TSK) model (PTFS) for data transfer by incorporating fuzzy cognitive maps (FCM). It imparts the TSK model with the data processing capability akin to deep learning models, thereby mitigating the interpretability loss arising from an increase in model depth. Furthermore, the multi-prototypes fuzzy system for decision making (MPDFS) is constructed to map data onto classes. An enhanced gradient descent method with restriction mechanism of prototype position is designed for parameter optimization. The experiments underscore PT-SAFC’s achievement of a harmonious equilibrium between interpretability and classification accuracy. And, PT-SAFC maintains an advantage in classification performance even compared to deep learning methods. Furthermore, experiments validate PT-SAFC’s capability to manipulate data distribution to augment classification efforts.
References
[1]
Rabcan J., Levashenko V., Zaitseva E. and Kvassay M., Eeg signal classification based on fuzzy classifiers, IEEE Transactions on Industrial Informatics 18(2) (2021), 757–766.
[2]
Qin B., Chung F.-l., Nojima Y., Ishibuchi H. and Wang S., Fuzzy rule dropout with dynamic compensation for wide learning algorithm of tsk fuzzy classifier, Applied Soft Computing 127 (2022), 109410.
[3]
Das A., Bhardwaj K. and Patra S., Deep convolution neural network with automatic attribute profiles for hyperspectral image classification, Multimedia Tools and Applications (12).
[4]
Lauriola I., Lavelli A. and Aiolli F., An introduction to deep learning in natural language processing: Models, techniques, and tools, Neurocomputing 470 (2022), 443–456.
[5]
Zhou S., Chen Q. and Wang X., Fuzzy deep belief networks for semi-supervised sentiment classification, Neurocomputing 131 (may 5) (2014), 312–322.
[6]
Du G., Wang Z., Li C. and Liu P.X., A tsk-type convolutional recurrent fuzzy network for predicting driving fatigue, IEEE Transactions on Fuzzy Systems 29(8) (2021), 2100–2111 http://dx.doi.org/10.1109/TFUZZ.2020.2992856.
[7]
Guan C., Wang S. and Liew A.W.-C., Lip image segmentation based on a fuzzy convolutional neural network, IEEE Transactions on Fuzzy Systems 28(7) (2020), 1242–1251. https://doi.org/10.1109/TFUZZ.2019.2957708
[8]
Deng Y., Ren Z., Kong Y., Bao F. and Dai Q., A hierarchical fused fuzzy deep neural network for data classification, IEEE Transactions on Fuzzy Systems 25(4) (2017), 1006–1012. https://doi.org/10.1109/TFUZZ.2016.2574915
[9]
Qin B., Nojima Y., Ishibuchi H. and Wang S., Realizing deep high-order tsk fuzzy classifier by ensembling interpretable zero-order tsk fuzzy subclassifiers, IEEE Transactions on Fuzzy Systems 29(11) (2021), 3441–3455. https://doi.org/10.1109/TFUZZ.2020.3022574
[10]
Zhang Y., Ishibuchi H. and Wang S., Deep takagi–sugeno–kang fuzzy classifier with shared linguistic fuzzy rules, IEEE Transactions on Fuzzy Systems 26(3) (2018), 1535–1549. https://doi.org/10.1109/TFUZZ.2017.2729507
[11]
Zhou T., Chung F.-L. and Wang S., Deep tsk fuzzy classifier with stacked generalization and triplely concise interpretability guarantee for large data, IEEE Transactions on Fuzzy Systems 25(5) (2017), 1207–1221. https://doi.org/10.1109/TFUZZ.2016.2604003
[12]
Zhou T., Wang G., Choi K.-S. and Wang S., Recognition of sleep-wake stages by deep takagi-sugeno-kang fuzzy classifier with random rule heritage, IEEE Transactions on Emerging Topics in Computational Intelligence (2023), 1–12. https://doi.org/10.1109/TETCI.2022.3233045
[13]
Gu S., Chung F.-L. and Wang S., A novel deep fuzzy classifier by stacking adversarial interpretable tsk fuzzy subclassifiers with smooth gradient information, IEEE Transactions on Fuzzy Systems 28(7) (2020), 1369–1382. https://doi.org/10.1109/TFUZZ.2019.2919481
[14]
Wang Y., Liu H., Jia W., Guan S., Liu X. and Duan X., Deep fuzzy rule-based classification system with improved wangmendel method, IEEE Transactions on Fuzzy Systems (2021), 1–1. https://doi.org/10.1109/TFUZZ.2021.3098339
[15]
Kosko B., Fuzzy cognitive maps, International Journal of Man-Machine Studies 24(1) (1986), 65–75.
[16]
Lu W., Feng G., Liu X., Pedrycz W. and Yang J., Fast and effective learning for fuzzy cognitive maps: A method based on solving constrained convex optimization problems, IEEE Transactions on Fuzzy Systems PP(99) (2019), 1–1.
[17]
Feng G., Zhang L., Yang J. and Lu W., Long-term prediction of time series using fuzzy cognitive maps, Engineering Applications of Artificial Intelligence 102 (2021), 104274. https://doi.org/10.1016/j.engappai.2021.104274
[18]
Yu T., Gan Q., Feng G. and Han G., A new fuzzy cognitive maps classifier based on capsule network, Knowledge-Based Systems 250 (2022), 108950. https://doi.org/10.1016/j.knosys.2022.108950
[19]
Szwed P., Classification and feature transformation with fuzzy cognitive maps, Applied Soft Computing 105 (2021), 107271. https://doi.org/10.1016/j.asoc.2021.107271.
[20]
Zhang Y., Lin H., Yang X. and Long W., Combining expert weights for online portfolio selection based on the gradient descent algorithm, Knowledge-Based Systems 234 (2021), 107533. https://doi.org/10.1016/j.knosys.2021.107533.
[21]
Abuqaddom I., Mahafzah B.A. and Faris H., Oriented stochastic loss descent algorithm to train very deep multi-layer neural networks without vanishing gradients, Knowledge-Based Systems 230 (2021), 107391. https://doi.org/10.1016/j.knosys.2021.107391.
[22]
Knight C.J., Lloyd D.J. and Penn A.S., Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points, Applied Soft Computing 15 (2014), 193–202. https://doi.org/10.1016/j.asoc.2013.10.030
[23]
Wu D., Yuan Y., Huang J. and Tan Y., Optimize tsk fuzzy systems for regression problems: Minibatch gradient descent with regularization, droprule, and adabound (mbgd-rda), IEEE Transactions on Fuzzy Systems 28(5) (2020), 1003–1015. https://doi.org/10.1109/TFUZZ.2019.2958559
[24]
Wu K., Liu J., Liu P. and Shen F., Online fuzzy cognitive map learning, IEEE Transactions on Fuzzy Systems 29(7) (2021), 1885–1898. https://doi.org/10.1109/TFUZZ.2020.2988845
[25]
Papageorgiou E.I., Poczeta K., Yastrebov A., Laspidou C. Fuzzy cognitive maps and multistep gradient methods for prediction: applications to electricity consumption and stock exchange returns, in: International Conference on Intelligent Decision Technologies, Springer, (2017), pp. 501–511.
[26]
Kingma D., Ba J. Adam: A method for stochastic optimization, arXiv:1412.6980.
[27]
Bergstra J. and Bengio Y., Random search for hyper-parameter optimization, Journal of Machine Learning Research 13(1) (2012), 281–305.
[28]
Scholkopf B., Smola A.J. Learning with Kernels –SupportVector Machines, Regularization, Optimization and Beyond, MIT Press Cambridge, MA, 2002.
[29]
Breiman, Random forests, MACH LEARN 45(1) (-) (2001), 5–32. https://doi.org/10.1023/A:1010933404324.
[30]
Jin-Bin H.E., Hui-Nan F.U., Huang C.Y., Pan Y.C. Application of gaussian support vector machine in classification and recognition of furniture sheet, Automation & Instrumentation.
[31]
Adjei P., Sethi N., Souza C., Capretz M. Energy disaggregation using multilabel binarization and gaussian naive bayes classifier, (2020), pp. 0093–0100. http://dx.doi.org/10.1109/UEMCON51285.2020.9298157.
[32]
Specht D.F., Probabilistic neural networks, Neural Networks 3(1) (1990), 109–118. https://doi.org/10.1016/0893-6080(90)90049-Q.
[33]
Wei Z., Gaoliang P., Chuanhao L., Yuanhang C. and Zhujun Z., A new deep learning model for fault diagnosis with good antinoise and domain adaptation ability on raw vibration signals, Sensors 17(3) (2017), 425.
[34]
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V. et al.Scikit-learn: Machine learning in python, the Journal of Machine Learning Research 12 (2011), 2825–2830.
[35]
Bui Q.-T., Vo B., Do H.-A.N., Hung N.Q.V. and Snasel V., F-mapper: A fuzzy mapper clustering algorithm, Knowledge-Based Systems 189 (2020), 105107. https://doi.org/10.1016/j.knosys.2019.105107.
[36]
Liu Y., Li Z., Xiong H., Gao X., Wu J. Understanding of internal clustering validation measures, in: 2010 IEEE international conference on data mining, IEEE, (2010), pp. 911–916.
[37]
Davies D.L. and Bouldin D.W., A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI- 1(2) (1979), 224–227.
[38]
Cali'nski T. and Harabasz J., A dendrite method for cluster analysis, Communications in Statistics-theory and Methods 3(1) (1974), 1–27.
Recommendations
Fuzzy classifier design using genetic algorithms
A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the ...
Toward a development of general type-2 fuzzy classifiers applied in diagnosis problems through embedded type-1 fuzzy classifiers
AbstractNowadays, with the emergence of computer-aided systems, diagnosis problems are one of the most important application areas of artificial intelligence. The present paper is focused on a specific kind of computer-aided diagnosis system based on ...
A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease
This paper presents a new method based on fuzzy cognitive map (FCM) and possibilistic fuzzy c-means (PFCM) clustering algorithm for categorizing celiac disease (CD). CD is a complex disorder whose development is affected by genetics (HLA alleles) and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2024 – IOS Press. All rights reserved.
Publisher
IOS Press
Netherlands
Publication History
Published: 10 January 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024
Other Metrics
Citations
View Options
View options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in