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Object feature selection under high-dimension and few-shot data based on three-way decision

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Abstract

A feature selection method based on three-way decision is proposed to solve the problem of target recognition under high-dimension and few-shot data. The main research results of this paper include the following aspects: (1) aiming at the uncertainty of existing algorithm caused by uncertain data in high-dimension and few-shot data, based on the traditional filtering feature selection ReliefF algorithm, the algorithm is improved, according to the three-way decision thought, the single threshold of original algorithm is extended into two thresholds, to increase the rate of fault tolerance of the original algorithm, avoid features is too much or too little to eliminate. (2) To solve the problem of "dimension disaster" of high-dimension and few-shot data, the features are divided into three domains according to two thresholds and feature weights, and then the features in the boundary domain are further selected by combining the wrapper feature selection idea to reduce the dimension and obtain the optimal feature subset. Finally, the experimental results show that the recognition accuracy and F1 have been improved. Experiment 1 proved the fault tolerance rate and stability of the proposed algorithm, and Experiment 2 verified the advantages of the proposed algorithm in small sample conditions.

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References

  1. Tu, Z., Xie, W., Dauwels, J., Li, B., Yuan, J.: Semantic cues enhanced multi-modality multi-stream CNN for action recognition. IEEE Trans. Circuits Syst. Video Technol (T-CSVT) 29(5), 1423–1437 (2019)

    Article  Google Scholar 

  2. Xu, Y., Qiu, T.T.: Human activity recognition and embedded application based on convolutional neural network. J. Artif. Intell. Technol. 1(1), 51–60 (2020). https://doi.org/10.37965/jait.2020.0051

    Article  Google Scholar 

  3. Zhigang, Tu., Xie, W., Qin, Q., Veltkamp, R.C., Li, B., Yuan, J.: Multi-stream CNN: learning representations based on human-related regions for action recognition. Pattern Recogn. 79, 32–43 (2018)

    Article  Google Scholar 

  4. Zhigang, Tu., Li, H., Zhang, D., Dauwels, J., Li, B., Yuan, J.: Action-stage emphasized spatio-temporal VLAD for video action recognition. IEEE Trans. Image Process. (TIP) 28(6), 2799–2812 (2019)

    Article  MATH  Google Scholar 

  5. Chen, Y., Zhigang, Tu., Kang, Di., Chen, R., Bao, L., Zhang, Z., Yuan, J.: Joint hand-object 3d reconstruction from a single image with cross-branch feature fusion. IEEE Trans. Image Process. 30, 4008–4021 (2021)

    Article  Google Scholar 

  6. Ponce, H., Cevallos, C., Espinosa, R., Gutiérrez, S.: Estimation of low nutrients in tomato crops through the analysis of leaf images using machine learning. J. Artif. Intell. Technol. 1(2), 131–137 (2021). https://doi.org/10.37965/jait.2021.0006

    Article  Google Scholar 

  7. Ai, J., Tian, R., Luo, Q., Jin, J., Tang, B.: Multi-scale rotation-invariant haar-like feature integrated CNN-based ship detection algorithm of multiple-target environment in SAR imagery. IEEE Trans. Geosci. Remote Sens. 57(12), 10070–10087 (2019). https://doi.org/10.1109/TGRS.2019.2931308

    Article  Google Scholar 

  8. Zhou, Y., Tang, J., Wang, J.: Improved TFIDF feature selection algorithm based on information entropy. Comput. Eng. Appl. 43(35), 156–158, 171 (2007)

  9. Luukka, P.: Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 38(4), 4600–4607 (2011)

    Article  Google Scholar 

  10. Jiang, S.Y., Wang, L.X.: Efficient feature selection based on correlation measure between continuous and discrete features. Inf. Process. Lett. 116(2), 203–215 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  11. Silui, C., Yong, Z., Zhiyong, Y.: Research on feature selection method based on rough set. Comput. Eng. Appl. 21, 159–161 (2006)

    Google Scholar 

  12. Li, C.: Research on Feature Selection Algorithm Based on Rough Set. Shanxi University, Shanxi (2013)

    Google Scholar 

  13. Hu, J., Pedrycz, W., Wang, G.Y., et al.: Rough sets in distributed decision information systems. Knowl.-Based Syst. 94, 13–22 (2016)

    Article  Google Scholar 

  14. Zhang, Z.H., Tian, Y.Y., Bai, L., et al.: High-order covariate interacted Lasso for feature selection. Pattern Recognit. Lett. 87, 139–146 (2017)

    Article  Google Scholar 

  15. Wang, H., Wang, P., Deng, S., Li, H.: Improved relief weight feature selection algorithm based on relief and mutual information. Information 12(6), 228 (2021)

    Article  Google Scholar 

  16. Zheng, J., Zhu, H., Chang, F., Liu, Y.: An improved relief feature selection algorithm based on Monte-Carlo tree search. Syst. Sci. Control Eng. 7(1), 304–310 (2019)

    Article  Google Scholar 

  17. Eiras‐Franco, C., Guijarro‐Berdiñas, B., Alonso‐Betanzos, A., Bahamonde, A.: Scalable feature selection using ReliefF aided by locality‐sensitive Hashin. Int. J. Intell. Syst. (2021).

  18. Shuchun, Y., Zheng, L., Qiang, Z.: Feature selection based on hybrid genetic algorithm and mutual information analysis for high-dimensional small samples. Computer Appl. Softw. 37(1), 247–255 (2020)

    Google Scholar 

  19. Xu, X., Zhang, K., Wang, W.: A feature selection method for small sample data. Comput. Res. Dev. 55(10), 229–238 (2018)

    Google Scholar 

  20. Biglari, M., Mirzaei, F., Hassanpour, H.: Feature selection for small sample sets with high dimensional data using heuristic hybrid approach. Int. J. Eng. Trans. B Appl. 33(2), 213–220 (2020)

    Google Scholar 

  21. Yao, Y. Set-theoretic models of three-way decision. Granular Comput. 2020;1–16.

  22. Yao, Y. The geometry of three-way decision. Appl. Intell. 2021;1–28.

  23. Wang, Z., Yan, C.: Summary of image feature extraction methods. J. Jishou Univ. Nat. Sci. 5, 43–47 (2011)

    Google Scholar 

  24. Liu, L., Kuang, G.: Overview of texture feature extraction methods. J. Image Graph. 04, 622–635 (2009)

    Google Scholar 

  25. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. Stud. Media Commun. 3(6), 610–621 (1973)

    Google Scholar 

  26. Jiguang, H.: Texture analysis method with gray-gradient co-existing matrix. Acta Autom. Sin. 10(1), 22–25 (1984)

    Google Scholar 

  27. Li, Y., Meng, X.: Image texture feature extraction based on gabor filter. J. Changchun Univ. Technol. (Nat. Sci. Ed.) 29(1), 78–81 (2008)

    Google Scholar 

  28. Smith, A.R.: Color Gamut transform paris. In: Proceedings of the 5th annual conference on Computer graphics and interactive techniques, New York, 12(3): 12–18 (1978).

  29. Li, X.-X., Wang, J.-G., Wang, D., et al.: Segmentation method of Zizydate leaf spot based on HSV color space. Anhui Agric. Sci. Bull. 26(04), 85–87 (2020)

    Google Scholar 

  30. Niblack, W.: The QBIC project: querying images by content using color, texture, and shape. Int. Soc. Opt. Eng. 1908, 173–187 (1993)

    Google Scholar 

  31. Stricker, M.A., Orengo, M.: Similarity of color Images. Int. Soc. Opt. Eng. 2420, 381–392 (1995)

    Google Scholar 

  32. Kononenko, I., Šimec, E., Robnik Aikonja, M.: Overcoming the myopia of ininductive learning with RELIEFF. Appl. Intell. 7(1), 39–55 (1997)

    Article  Google Scholar 

  33. Guyon, I., Weston, J., Barnhill, S., et al.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)

    Article  MATH  Google Scholar 

  34. Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge National Natural Science Foundation of China (Grant No.61573285, No.62003267), Open Fund of Key Laboratory of Data Link Technology of China Electronics Technology Group Corporation (Grant No. CLDL-20182101) and Natural Science Foundation of Shaanxi Province (Grant No. 2020JQ-220) to provide fund for conducting experiments.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Kaifang Wan, Jianmei Wang & Bo Li

  2. School of Engineering, London South Bank University, London, SE1 0AA, UK

    Daqing Chen

  3. AVIC Xi’an Aeronautics Computing Technique Research Institute, Xi’an, 710068, China

    Linyu Tian

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  1. Kaifang Wan
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  2. Jianmei Wang
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Correspondence to Bo Li.

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Wan, K., Wang, J., Li, B. et al. Object feature selection under high-dimension and few-shot data based on three-way decision. Vis Comput 39, 2261–2275 (2023). https://doi.org/10.1007/s00371-022-02411-7

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