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Buffer-based adaptive fuzzy classifier

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Abstract

In the age of a technological revolution, heterogeneous sources are generating streams of data at a high rate, and an online classification of these data can facilitate data mining and analysis. Among the available classifiers, fuzzy-system-based (FSB) classifiers provide remarkable contributions due to their antecedent-consequent rule base structure. The Mamdani and Takagi-Sugeno type structure always uses the identical antecedent portion with fuzzy sets, which are themselves specified by parameterized membership functions driven by logical AND/OR operations. These membership functions are discerned either by experts or from data. However, for online or stream data, using a predefined membership function is not ideal. Meanwhile, a data-cloud has the ability to adopt changes in stream data, which share the same properties as those of a cluster but does not have any predefined shapes or a particular radius; rather, data-cloud offer a more objective representation of real-time data. Moreover, most algorithms with FSB classifiers avoid the presence of temporarily irrelevant data points or data-clouds that can be relevant in the future. In this paper, we develop a novel data-cloud-based classification algorithm for stream data classification called buffer-based adaptive fuzzy classifier (BAFC). The offline training stage of this algorithm can identify data-cloud from a static dataset to construct the AnYa type fuzzy rule. This algorithm is also able to cope with the dynamic nature of stream data. At the online or one-pass training stage, BAFC updates its rule base by creating and merging data-cloud based on its potential area. This algorithm also introduces a recursive formula for calculating data-cloud density with a buffer that is used for storing temporarily irrelevant data clouds. BAFC also uses the online pruning system of data-clouds to address storage problems. This approach can solve the issues associated with the parameterization and redundant rule base for other types of stream data (e.g., sensor data, bank transaction, intruder detection, images and videos, and, stock market and disease prediction) classification algorithms. This two-stage algorithm is evaluated on several benchmark datasets, and the results prove its superiority over different well-established classifiers in terms of classification accuracy (90.82% for 6 datasets and 97.13% for the MNIST dataset), memory efficiency (twice higher than other classifiers), and efficiency in addressing high-dimensional problems.

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References

  1. Lughofer E (2021) Improving the robustness of recursive consequent parameters learning in evolving neuro-fuzzy systems. In: Information sciences. vol 545, pp 555–574

  2. Ge D, Zeng X-J (2020) Learning data streams online–An evolving fuzzy sys- tem approach with self-learning/adaptive thresholds. In: Information sciences. vol 507, pp 172–184

  3. Hariri RH, Fredericks EM, Bowers KM (2019) Uncertainty in big data analytics: survey, opportunities, and challenges. In: Journal of big data 6.1, pp 44

  4. Gu X, Angelov PP (2018) Self-organising fuzzy logic classifier. In: Information Sciences. vol 447, pp 36–51

  5. Cunningham P, Delany SJ (2021) k-Nearest neighbour classifiers-A tutorial. In: ACM computing surveys (CSUR) 54.6, pp 1–25

  6. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer science & business media

  7. Safavian SR, Landgrebe D (1991) A survey of decision tree classifiser methodology. In: IEEE transactions on systems, man, and cybernetics 21.3, pp 660–674

  8. Subudhi A, Dash M, Sabut S (2020) Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. In: Biocybernetics and biomedical engineering 40.1, pp 277–289

  9. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. In: Applied soft computing. vol 88, p 105946

  10. Gibert D et al (2019) Using convolutional neural networks for classification of malware represented as images. In: Journal of computer virology and hacking techniques 15.1, pp 15– 28

  11. Yadav SS, Jadhav SM (2019) Deep convolutional neural network based medical image classification for disease diagnosis. In: Journal of big data 6.1, pp 1–18

  12. Alimjan G et al (2018) A new technique for remote sensing image classification based on combinatorial algorithm of SVM and KNN. In: International journal of pattern recognition and artificial intelligence 32.07, p 1859012

  13. Cervantes J et al (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. In: Neurocomputing, vol 408, pp 189–215

  14. Fahim SR et al (2020) Microgrid fault detection and classification: machine learning based approach, comparison, and reviews. In: Energies 13.13, pp 3460

  15. Dash S et al (2019) A Neuro-fuzzy approach for user behaviour classification and prediction. In: Journal of cloud computing 8.1, pp 1–15

  16. Assegie TA, Nair PS (2019) Handwritten digits recognition with decision tree classification: a machine learning approach. In: International journal of electrical and computer engineering (IJECE) 9.5, pp 4446–4451

  17. Ali W et al (2021) Classical and modern face recognition approaches: a complete review. In: Multimedia tools and applications 80.3, pp 4825–4880

  18. Yeganejou Mojtaba, Dick Scott, Miller James (2019) Interpretable deep convolutional fuzzy classifier. In: IEEE transactions on fuzzy systems 28.7, pp 1407–1419

  19. Lughofer E, Pratama M, Škrjanc I (2021) Online bagging of evolving fuzzy systems. In: Information sciences 570, pp 16–33

  20. Muthugala MA et al (2020) A self-organizing fuzzy logic classifier for benchmarking robot-aided blasting of ship hulls. In: Sensors 20.11, p 3215

  21. Tsakiridis NL et al (2019) An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries. In: Applied soft computing 81, pp 105504

  22. Kangin Dmitry, Angelov Plamen, Iglesias JA (2016) Autonomously evolving classifier TEDAClass. In: Information sciences 366, pp 1–11

  23. Angelov PP, Zhou Xiaowei (2008) Evolving fuzzy-rule-based classifiers from data streams. In: Ieee transactions on fuzzy systems 16.6, pp 1462–1475

  24. Angelov Plamen P, Gu Xiaowei, Principe JC (2017) Autonomous learning multimodel systems from data streams. In: IEEE transactions on fuzzy systems 26.4, pp 2213–2224

  25. Noorbehbahani Fakhroddin et al (2017) An incremental intrusion detection system using a new semi-supervised stream classification method. In: International journal of communication systems 30.4, pp e3002

  26. Iglesias JA et al (2011) Creating evolving user behavior profiles automatically. In: IEEE transactions on knowledge and data engineering 24.5, pp 854–867

  27. Angelov Plamen, Yager Ronald (2012) A new type of simplified fuzzy rule-based system. In: International journal of general systems 41.2, pp 163–185

  28. Senoussaoui M et al (2013) Efficient iterative mean shift based cosine dissimilarity for multi-recording speaker clustering. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 7712–7715

  29. Aggarwal CC, Hinneburg Alexander, Keim Daniel (2001) A On the surprising behavior of distance metrics in high dimensional space. In: International conference on database theory. Springer, pp 420–434

  30. Prim RC (1957) Shortest connection networks and some generalizations. In: The bell system technical journal 36.6, pp 1389–1401

  31. Angelov P et al (2016) Empirical data analysis: a new tool for data analytics. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 000052–000059

  32. Angelov P, Zhou X, Klawonn Frank (2007) Evolving fuzzy rule-based classifiers. In: 2007 IEEE symposium on computational intelligence in image and signal processing. IEEE, pp. 220–225

  33. Shahparast Homeira, Mansoori EG, Jahromi MZ (2019) AFCGD: an adaptive fuzzy classifier based on gradient descent. In: Soft computing 23.12, pp 4557–4571

  34. Ahmed Usman, Lin* JC-W, Srivastava Gautam (2022) Fuzzy contrast set based deep attention network for lexical analysis and mental health treatment. In: Transactions on asian and low-resource language information processing

  35. Wu M-E et al (2021) Effective fuzzy system for qualifying the characteristics of stocks by random tradings. In: IEEE transactions on fuzzy systems

  36. Wu T-Y et al (2020) An efficient algorithm for fuzzy frequent itemset mining. In: Journal of intelligent & fuzzy systems 38.5, pp 5787–5797

  37. Qasem SN et al (2021) A type-3 logic fuzzy system: Optimized by a correntropy based Kalman filter with adaptive fuzzy kernel size. In: Information sciences. vol 572, pp 424–443

  38. Wang J-h et al (2021) Non-singleton type-3 fuzzy approach for flowmeter fault detection: experimental study in a gas industry. In: Sensors 21.21, pp 7419

  39. Anter AM et al (2020) A new type of fuzzy-rule-based system with chaotic swarm intelligence for multiclassification of pain perception from fMRI. In: IEEE transactions on fuzzy systems 28.6, pp. 1096–1109

  40. Angelov P, Gu X, Kangin D (2017) Empirical data analytics. In: International journal of intelligent systems 32.12, pp 1261–1284

  41. Gu X, Angelov PP, Principe JC (2018) A method for autonomous data par- titioning. In: Information sciences. vol 460, pp 65–82

  42. de Campos Souza PV, Wang Y-K, Lughofer E (2020) Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural net- work. In: 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, pp 1–8

  43. Calabrese F et al (2020) Unsupervised fault detection and prediction of remaining useful life for online prognostic health management of mechanical systems. In: Applied sciences 10.12, pp 4120

  44. Angelov PP, Gu X (2018) Empirical fuzzy sets. In: International journal of intelligent systems 33.2, pp 362–395

  45. Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 3 March 2022

  46. Boots B, Okabe A, Sugihara K (1999) Spatial tessellations. In: Geographical information systems vol 1, pp 503–526

  47. Islam MK, Ahmed MM, Zamli KZ (2019) A buffer-based online clustering for evolving data stream. In: Information sciences, vol 489, pp 113–135

  48. Cristianini Nello, Shawe-Taylor John, et al. (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press

  49. Płoński P, Zaremba K (2012) Self-organising maps for classification with metropolis-hastings algorithm for supervision. In: International conference on neural information processing. Springer, pp 149–156

  50. Kasabov NK, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. In: IEEE transactions on fuzzy systems 10.2, pp 144–154

  51. Candanedo LM, Feldheim V (2016) Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. In: Energy and buildings 112, pp. 28–39

  52. Street WN, Kim Y (2001) A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp 377–382

  53. Holmes RKABG, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601–1604. http://portal.acm.org/citation.cfm?id=1859903. Accessed 3 March 2022

    Google Scholar 

  54. Cortes C, LeCun Y MNIST handwritten digit database. http://yann.lecun.com/exdb/mnist/,in:2010. Accessed 3 March 2022

  55. Angelov P et al (2014) Symbol recognition with a new autonomously evolving classifier au- toclass. In: 2014 IEEE conference on evolving and adaptive intelligent systems (EAIS). IEEE, pp 1–7

  56. Verma M, Raman B (2018) Local neighborhood difference pattern: a new feature descriptor for natural and texture image retrieval. In: Multimedia tools and applications 77.10, pp 11843–11866

  57. Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 270–279

  58. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, vol 25

  59. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, pp 1–14

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Acknowledgements

The authors would like to acknowledge the Qatar National Library for the Open Access funding. This work was also supported by Special Grant of ICT Division (Ministry of Posts, Telecommunications and Information Technology), Bangladesh, and Grant No. 56.00.0000.028.20.004.20-333.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Barishal, Barishal-8254, Barishal, Bangladesh

    Sajal Debnath & Md Manjur Ahmed

  2. Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

    Samir brahim Belhaouari

  3. Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

  4. Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh

    Mostafijur Rahman

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  1. Sajal Debnath
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  2. Md Manjur Ahmed
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Correspondence to Md Manjur Ahmed or Samir brahim Belhaouari.

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Debnath, S., Ahmed, M.M., Belhaouari, S.b. et al. Buffer-based adaptive fuzzy classifier. Appl Intell 53, 14448–14469 (2023). https://doi.org/10.1007/s10489-022-04155-2

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