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A novel learning method for feature evolvable streams

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Abstract

Recently, many researchers have focused on a novel type of data stream known as a feature evolvable stream, wherein the existing features may become obsolete while new features emerge simultaneously. The occurrence of this phenomenon leads to a degradation in the classification ability of the constructed model. To address this problem, we propose a novel method FENSL for classifying feature evolvable streams. First, we employ fuzzy membership values to enhance the accuracy and reliability of model. Then, we utilize a twin support vector machine to train a classification model on the instances with their existing features. Moreover, as new features emerge, we develop a mapping matrix between two heterogeneous feature spaces through the locally weighted linear regression algorithm. This enables our previously well-trained model to effectively adapt to the new feature space. Finally, by reusing the old model, we construct a stable classification model that is capable of handling data with new features. Experimental results obtained on synthetic datasets show that our proposed method exhibits adaptability in terms of learning from feature evolvable streams and demonstrates great antinoise performance.

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Data availability

The data that support the findings of this study are openly available in UCI Machine Learning Repository at http://archive.ics.uci.edu.

Notes

  1. http://archive.ics.uci.edu/ml/datasets/Credit+Approval.

  2. http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data).

  3. http://archive.ics.uci.edu/ml/machine-learning-databases/statlog.

  4. http://archive.ics.uci.edu/ml/datasets/Chess+(King-Rook+vs.+King-Pawn).

  5. http://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Splice-junction+Gene+Sequences).

References

  • Baker SB, Xiang W, Atkinson I (2017) Internet of things for smart healthcare: technologies, challenges, and opportunities. IEEE Access 5:26521–26544

    Article  Google Scholar 

  • Beyazit E, Alagurajah J, Wu X (2019) Online learning from data streams with varying feature spaces. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. AAAI Press, pp 3232–3239

  • Chen B, Fan Y, Lan W et al (2022) Fuzzy support vector machine with graph for classifying imbalanced datasets. Neurocomputing 514:296–312

    Article  Google Scholar 

  • Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83(403):596–610

    Article  Google Scholar 

  • Day O, Khoshgoftaar TM (2017) A survey on heterogeneous transfer learning. J Big Data 4:1–42

    Article  Google Scholar 

  • Dhanasekaran Y, Murugesan P (2022) Improved bias value and new membership function to enhance the performance of fuzzy support vector machine. Expert Syst Appl 208:118003

    Article  Google Scholar 

  • Dong J, Cong Y, Sun G et al (2022) Evolving metric learning for incremental and decremental features. IEEE Trans Circ Syst Video Technol 32(4):2290–2302

    Article  Google Scholar 

  • Gao BB, Wang JJ (2017) A fast and robust TSVM for pattern classification. arXiv:1711.05406

  • He Y, Wu B, Wu D, Beyazit E, Wu X (2020) Toward mining capricious data streams: a generative approach. IEEE Trans Neural Netw Learn Syst 99:1–13

    Google Scholar 

  • He Y, Yuan X, Chen S et al (2021) Online learning in variable feature spaces under incomplete supervision, pp 4106–4114

  • Hou C, Zhou ZH (2018) One-pass learning with incremental and decremental features. IEEE Trans Pattern Anal Mach Intell 40(11):2776–2792

    Article  Google Scholar 

  • Hou BJ, Yan YH, Zhao P et al (2021a) Storage fit learning with feature evolvable streams. Proc AAAI Conf Artif Intell 35(9):7729–7736

  • Hou B-J, Zhang L, Zhou Z-H (2021b) Learning with feature evolvable streams. IEEE Trans Knowl Data Eng 33:2602–2615

  • Hou BJ, Zhang L, Zhou ZH (2022) Prediction with unpredictable feature evolution. IEEE Trans Neural Netw Learn Syst 33:5706–5715

    Article  MathSciNet  Google Scholar 

  • Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910

    Article  Google Scholar 

  • Li J, Wang X, Zeng Z (2024) A dual-stream recurrence-attention network with global-local awareness for emotion recognition in textual dialog. Eng Appl Artif Intell 128:107530

    Article  Google Scholar 

  • Lian H, Atwood JS, Hou B et al (2022) Online deep learning from doubly-streaming data, pp 3185–3194

  • Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471

    Article  Google Scholar 

  • Liu Zhaoqing G, Shilin HC (2022a) Online classification algorithm for inheritance enhancement and subtraction of feature. J Comput Res Dev 59(08):1668–1682

  • Liu Y, Fan X, Li W et al (2022b) Online passive-aggressive active learning for trapezoidal data streams. IEEE Trans Neural Netw Learn Syst 34:6725–6739

  • Maldonado S, Lopez J, Vairetti C (2021) Time-weighted fuzzy support vector machines for classification in changing environments. Inf Sci 559:97–110

    Article  MathSciNet  Google Scholar 

  • Manzoor EA, Lamba H, Akoglu L (2018) xStream: outlier detection in feature-evolving data streams, pp 1963–1972

  • Saffari A, Khishe M, Zahiri SH (2022a) Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network. Analog Integr Circ Signal Process 111(3):403–417

  • Saffari A, Zahiri SH, Khishe M (2022b) Fuzzy grasshopper optimization algorithm: a hybrid technique for tuning the control parameters of GOA using fuzzy system for big data sonar classification. Iran J Electr Electron Eng 18(1):1–12

  • Saffari A, Zahiri SH, Khishe M (2023) Fuzzy whale optimisation algorithm: a new hybrid approach for automatic sonar target recognition. J Exp Theor Artif Intell 35(2):309–325

    Article  Google Scholar 

  • Tapus A, Bandera A, Vazquez-Martin R, Calderita LV (2019) Perceiving the person and their interactions with the others for social robotics—a review. Pattern Recognit Lett 118:3–13

    Article  Google Scholar 

  • Tsai YHH, Yeh YR, Wang YCF (2016) Heterogeneous domain adaptation with label and structure consistency. 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2842–2846

  • Wang C, Xie L, Wang W et al (2020) Probing into the physical layer: moving tag detection for large-scale RFID systems. IEEE Trans Mob Comput 19(5):1200–1215

    Article  Google Scholar 

  • Wang L, Khishe M, Mohammadi M et al (2022) Extreme learning machine evolved by fuzzified hunger games search for energy and individual thermal comfort optimization. J Build Eng 60:105187

    Article  Google Scholar 

  • Wen Y, LIU S, MIAO Y et al (2022) Review on semi-supervised classification of concept drift data streams. J Softw 33(04):1287–1314

    Google Scholar 

  • Wu H, Yan Y, Ye Y et al (2019) Online heterogeneous transfer learning by knowledge transition. ACM Trans Intell Syst Technol (TIST) 10(3):1–19

    Article  Google Scholar 

  • Xie W, Zhu F, Jiang J et al (2016) Topicsketch: real-time bursty topic detection from twitter. IEEE Trans Knowl Data Eng 28(8):2216–2229

    Article  Google Scholar 

  • Xing H, Xiao Z, Zhan D et al (2022a) SelfMatch: robust semisupervised time-series classification with self-distillation. Int J Intell Syst 37(11):8583–8610

  • Xing H, Xiao Z, Qu R et al (2022b) An efficient federated distillation learning system for multitask time series classification. IEEE Trans Instrum Meas 71:1–12

  • Xu C, Tao D, Xu C (2013) A survey on multi-view learning. arXiv:1304.5634

  • Yan Y, Li W, Wu H et al (2018a) Semi-supervised optimal transport for heterogeneous domain adaptation. IJCAI 7:2969–2975

  • Yan Y, Wu Q, Tan M et al (2018b) Online heterogeneous transfer by hedge ensemble of offline and online decisions. IEEE Trans Neural Netw Learn Syst 29(7):3252–3263

  • Yanfang L, Wenbin L, Yang G (2021) Feature evolution flow learning based on passive-active. J Comput Res Dev 58(08):1575–1585

    Google Scholar 

  • Yanfang L, Wenbin L, Yang G (2022) A confidence-weighted learning method for feature evolution. J Softw 33(04):1315–1325

    Google Scholar 

  • Ye HJ, Sheng XR, Zhan DC et al (2018a) Distance metric facilitated transportation between heterogeneous domains. IJCAI, pp 3012–3018

  • Ye HJ, Zhan DC, Jiang Y et al (2018b) Rectify heterogeneous models with semantic mapping. International conference on machine learning. PMLR, pp 5630–5639

  • Yutong G, Khishe M, Mohammadi M et al (2022) Evolving deep convolutional neural networks by extreme learning machine and fuzzy slime mould optimizer for real-time sonar image recognition. In J Fuzzy Syst 24(3):1371–1389

    Article  Google Scholar 

  • Zhang Z, Zhao P, Jiang Y et al (2020) Learning with feature and distribution evolvable streams, pp 11317–11327

Download references

Acknowledgements

This work was supported by the projects from the Anhui Provincial Natural Science Foundation under Grant No. 2308085MF220, the University Natural Science Research Projects of Anhui Province under Grant Nos. 2022AH050972 and KJ2021A0516, the School-enterprise Cooperation of Anhui Polytechnic University under Grant No. 2023qyhz15, Anhui Future Technology Research Institute Enterprise Cooperation Project under Grant No. 2023qyhz12.

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Authors and Affiliations

  1. School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, Anhui, China

    Yanfei Chen & Sanmin Liu

  2. Industrial Innovation Technology Research Co. Ltd., Anhui Polytechnic University, Wuhu, 241000, Anhui, China

    Sanmin Liu

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  1. Yanfei Chen
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  2. Sanmin Liu
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Correspondence to Sanmin Liu.

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Chen, Y., Liu, S. A novel learning method for feature evolvable streams. Evolving Systems (2024). https://doi.org/10.1007/s12530-024-09590-9

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