research-article

Mutual Supervised Fusion & Transfer Learning with Interpretable Linguistic Meaning for Social Data Analytics

Authors:

Yuanpeng Zhang,

Jolfaei AlirezaAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 5

Article No.: 152, Pages 1 - 20

https://doi.org/10.1145/3568675

Published: 09 May 2023 Publication History

Abstract

Social data analytics is often taken as the most commonly used method for community discovery, product recommendations, knowledge graph, and so on. In this study, social data are firstly represented in different feature spaces by using various feature extraction algorithms. Then we build a transfer learning model to leverage knowledge from multiple feature spaces. During modeling, since the assumption that the training and the testing data have the same distribution is always true, we give a theorem and its proof which asserts the necessary and sufficient condition for achieving a minimum testing error. We also theoretically demonstrate that maximizing the classification error consistency across different feature spaces can improve the classification performance. Additionally, the cluster assumption derived from semi-supervised learning is introduced to enhance knowledge transfer. Finally, a Tagaki-Sugeno-Kang (TSK) fuzzy system-based learning algorithm is proposed, which can generate interpretable fuzzy rules. Experimental results not only demonstrate the promising social data classification performance of our proposed approach but also show its interpretability which is missing in many other models.

References

[1]

F. H. Lopes da Silva. 2008. The impact of EEG/MEG signal processing and modeling in the diagnostic and management of epilepsy. IEEE Reviews in Biomedical Engineering 1 (2008), 143–156.

[2]

C. Stamoulis and B. S. Chang. 2013. Modeling noninvasive neurostimulation in epilepsy as stochastic interference in brain networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering 21, 3 (May 2013), 354–363.

[3]

M. Peker, B. Sen, and D. Delen. 2016. A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE Journal of Biomedical and Health Informatics 20, 1 (Jan. 2016), 108–118.

[4]

D. Wu, V. J. Lawhern, W. D. Hairston, and B. J. Lance. 2016. Switching EEG headsets made easy: Reducing offline calibration effort using active weighted adaptation regularization. IEEE Transactions on Neural Systems and Rehabilitation Engineering 24, 11 (Nov. 2016), 1125–1137.

[5]

D. Wu, V. J. Lawhern, S. Gordon, B. J. Lance, and C. Lin. 2017. Driver drowsiness estimation from EEG signals using online weighted adaptation regularization for regression (OwARR). IEEE Transactions on Fuzzy Systems 25, 6 (Dec. 2017), 1522–1535.

Digital Library

[6]

X. Xu, W. Li, D. Xu, and I. W. Tsang. 2016. Co-labeling for multi-view weakly labeled learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 6 (1 June 2016), 1113–1125.

[7]

C. Xu, D. Tao, and C. Xu. 2015. Multi-view intact space learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 12 (1 Dec. 2015), 2531–2544.

Digital Library

[8]

Y. Li, M. Yang, and Z. Zhang. 2019. A survey of multi-view representation learning. In IEEE Transactions on Knowledge and Data Engineering 31, 10 (1 Oct. 2019), 1863–1883.

[9]

C. Yang, Z. Deng, K.-S. Choi, Y. Jiang, and S. Wang. 2014. Transductive domain adaptive learning for epileptic electroencephalogram recognition. Artif. Intell. Med. 62, 3 (Nov. 2014), 165–177.

[10]

Y. Jiang et al. 2017. Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, 12 (Dec. 2017), 2270–2284.

[11]

Y. Jiang, Y. Zhang, C. Lin, D. Wu, and C.-T. Lin. 2021. EEG-based driver drowsiness estimation using an online multi-view and transfer TSK fuzzy system. IEEE Transactions on Intelligent Transportation Systems 22, 3 (2021), 1752–1764. DOI:

Digital Library

[12]

Y. Jiang, F. Chung, H. Ishibuchi, Z. Deng, and S. Wang. 2015. Multitask TSK fuzzy system modeling by mining intertask common hidden structure. IEEE Transactions on Cybernetics 45, 3 (March 2015), 534–547.

[13]

A. Argyriou, T. Evgeniou, and M. Pontil. 2007. Multi-task feature learning. In NIPS. MIT Press, (2007), 41.

[14]

W. Dai, G.-R. Xue, Q. Yang, and Y. Yu. 2007. Co-clustering based classification for out-of-domain documents. In KDD, (2007), 210–219.

[15]

W. Dai, Q. Yang, G. Xue, and Y. Yu. 2008. Self-taught clustering. In ICML, (2008), 200–207.

[16]

L. Duan, I. Tsang, D. Xu, and S. Maybank. 2009. Domain transfer SVM for video concept detection. CVPR, 1375–1381, (2009).

[17]

S. Pan, J. Kwok, and Q. Yang. Transfer learning via dimensionality reduction. In Proceedings of AAAI, 677–682.

Digital Library

[18]

R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng. 2007. Self-taught learning: Transfer learning from unlabeled data. In ICML, (2007), 766–763.

[19]

E. Bonilla, K. Chai, and C. Williams. 2008. Multi-task Gaussian process prediction. NIPS 20, (2008), 153–160.

[20]

N. Lawrence and J. Platt. 2004. Learning to learn with the informative vector machine. In ICML, (2004), 65.

[21]

W. Dai, Q. Yang, G. Xue, and Y. Yu. 2007. Boosting for transfer learning. In ICML, (2007), 200–207.

[22]

B. Zadrozny. 2004. Learning and evaluating classifiers under sample selection bias. In ICML, (2004).

[23]

W. Hang, S. Liang, K.-S. Choi, F.-L. Chung, and S. Wang. 2021. Selective transfer classification learning with classification-error-based consensus regularization. IEEE Transactions on Emerging Topics in Computational Intelligence 5, 2 (2021), 178–190. DOI:

[24]

J. Davis and P. Domingos. 2009. Deep transfer via second-order Markov logic. In ICML, (2009), 217–224.

[25]

L. Mihalkova, T. Huynh, and R. Mooney. 2007. Mapping and revising Markov logic networks for transfer learning. In AAAI 22, (2007), 608–613.

[26]

L. Mihalkova and R. Mooney. 2008. Transfer learning by mapping with minimal target data. In Proceedings of the AAAI-08 Workshop on Transfer Learning for Complex Tasks.

[27]

J. Xu, J. Han, F. Nie, and X. Li. 2020. Multi-view scaling support vector machines for classification and feature selection. IEEE Transactions on Knowledge and Data Engineering 32, 7 (2020), 1419–1430. DOI:

[28]

C. Xu, D. Tao, and C. Xu. 2015. Multi-view intact space learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 12 (1 Dec. 2015), 2531–2544.

Digital Library

[29]

Y. Wang, X. Lin, L. Wu, W. Zhang, Q. Zhang, and X. Huang. 2015. Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Transactions on Image Processing 24, 11 (Nov. 2015), 3939–3949.

Digital Library

[30]

G. Li, K. Chang, and S. C. H. Hoi. 2012. Multiview semi-supervised learning with consensus. IEEE Transactions on Knowledge and Data Engineering 24, 11 (Nov. 2012), 2040–2051.

Digital Library

[31]

K. Ganchev, J. Graça, J. Blitzer, and B. Taskar. 2008. Multi-view learning over structured and non-identical outputs. In UAI, (2008), 204–211.

[32]

D. Zhang, J. He, Y. Liu, L. Si, and R. Lawrence. 2011. Multi-view transfer learning with a large margin approach. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11). Association for Computing Machinery, New York, NY, USA, 1208–1216.

Digital Library

[33]

C. Briat. 2011. Convergence and equivalence results for the Jensen's inequality—application to time-delay and sampled-data systems. IEEE Transactions on Automatic Control 56, 7 (July 2011), 1660–1665.

[34]

C. Yang, Z. Deng, K. Choi, and S. Wang. 2016. Takagi–Sugeno–Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals. IEEE Transactions on Fuzzy Systems 24, 5 (Oct. 2016), 1079–1094.

[35]

X. Tian et al. 2019. Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 10 (Oct. 2019), 1962–1972.

[36]

Z. Deng, L. Cao, Y. Jiang, and S. Wang. 2015. Minimax probability TSK fuzzy system classifier: A more transparent and highly interpretable classification model. IEEE Transactions on Fuzzy Systems 23, 4 (Aug. 2015), 813–826.

Digital Library

[37]

M. Rouhani, M. Mohammadi, and A. Kargarian. 2016. Parzen window density estimator-based probabilistic power flow with correlated uncertainties. IEEE Transactions on Sustainable Energy 7, 3 (July 2016), 1170–1181.

[38]

J. Alcal-Fdez, et al. 2011. KEEL data-mining software tool: Data set repository integration of algorithms and experimental analysis framework. J. Multiple-Valued Log. Soft Comput. 17, 2 (2011), 255–287.

[39]

A. Subasi and M. I. Gursoy. 2010. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37, 12 (Dec. 2010), 8659–8666.

Digital Library

[40]

Y. Jiang et al. 2017. Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system. IEEE Transactions on Fuzzy Systems 25, 1 (Feb. 2017), 3–20.

[41]

M. Long, J. Wang, G. Ding, D. Shen, and Q. Yang. 2014. Transfer learning with graph co-regularization. IEEE Trans. Knowl. Data Eng. 26, 7 (Jul. 2014), 1805–1818.

[42]

M. Long, J. Wang, G. Ding, S. J. Pan, and P. S. Yu. 2014. Adaptation regularization: A general framework for transfer learning. IEEE Transactions on Knowledge and Data Engineering 26, 5 (May 2014), 1076–1089.

Digital Library

[43]

J. Demar. 2006. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006), 1–30.

Digital Library

[44]

S. Holm. 1979. A simple sequentially rejective multiple test procedure. Scand. J. Statist. 6, 2 (1979), 65–70.

[45]

Y. Shi, C. Lin, Y. Chang, W. Ding, Y. Shi, and X. Yao. Consensus learning for distributed fuzzy neural network in big data environment. In IEEE Transactions on Emerging Topics in Computational Intelligence. DOI:

[46]

W. Ding, J. Nayak, B. Naik, D. Pelusi, and M. Mishra. Fuzzy and real coded chemical reaction optimization for intrusion detection in industrial big data environment. In IEEE Transactions on Industrial Informatics. DOI:

[47]

W. Ding, M. Abdel-Basset, K. A. Eldrandaly, L. Abdel-Fatah, and V. H. C. de Albuquerque. Smart supervision of cardiomyopathy based on fuzzy Harris Hawks Optimizer and wearable sensing data optimization: A new model. In IEEE Transactions on Cybernetics. DOI:

[48]

C. Cheng, W. Ding, F. Xiao, and W. Pedrycz. A majority rule-based measure for Atanassov type intuitionistic membership grades in MCDM. In IEEE Transactions on Fuzzy Systems. DOI:

Digital Library

[49]

W. Ding, C.-T. Lin, and Z. Cao. 2019. Shared nearest-neighbor quantum game-based attribute reduction with hierarchical coevolutionary spark and its application in consistent segmentation of neonatal cerebral cortical surfaces. In IEEE Transactions on Neural Networks and Learning Systems 30, 7 (July 2019), 2013–2027.

[50]

W. Ding. 2019. SVM-based feature selection for differential space fusion and its application to diabetic fundus image classification. In IEEE Access 7 (2019), 149493–149502.

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Index Terms

Mutual Supervised Fusion & Transfer Learning with Interpretable Linguistic Meaning for Social Data Analytics
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 5

May 2023

653 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3596451

Editor:
Imed Zitouni
Google, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 09 May 2023

Online AM: 20 October 2022

Accepted: 14 October 2022

Revised: 07 October 2022

Received: 02 June 2022

Published in TALLIP Volume 22, Issue 5

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National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Jiangsu Postdoctoral Research Funding Program

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Cited By

Lu WWei SPeng XWang YNaseem UWang S(2024)Medical Question Summarization with Entity-driven Contrastive LearningACM Transactions on Asian and Low-Resource Language Information Processing10.1145/365216023:4(1-19)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3652160
Dong YXiao L(2024)Enhancing wind power generation prediction using relevance assessment-based transfer learningKnowledge-Based Systems10.1016/j.knosys.2024.112417(112417)Online publication date: Aug-2024
https://doi.org/10.1016/j.knosys.2024.112417
Ding YCao YJia MDing PZhao XLee C(2024)Deep temporal–spectral domain adaptation for bearing fault diagnosisKnowledge-Based Systems10.1016/j.knosys.2024.111999299(111999)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.111999
Zhou W(2024)Dilemma and coping strategies of news communication based on artificial intelligence and big dataHeliyon10.1016/j.heliyon.2024.e2539810:3(e25398)Online publication date: Feb-2024
https://doi.org/10.1016/j.heliyon.2024.e25398

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