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Quantum-enhanced feature selection with forward selection and backward elimination

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Abstract

Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

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References

  1. Ordonez, V., Han, X., Kuznetsova, P., Kulkarni, G., Mitchell, M., Yamaguchi, K., Stratos, K., Goyal, A., Dodge, J., Mensch, A., et al.: Large scale retrieval and generation of image descriptions. Int. J. Comput. Vis. 119, 46–59 (2016)

    Article  MathSciNet  Google Scholar 

  2. Li, P., Shrivastava, A., Moore, J.L., König, A.C.: Hashing algorithms for large-scale learning. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 2672–2680. Curran Associates, Inc., Granada, Spain (2011)

  3. Lee, C.P., Leu, Y.: A novel hybrid feature selection method for microarray data analysis. Appl. Soft Comput. 11, 208–213 (2011)

    Article  Google Scholar 

  4. Blum, B., Baker, D., Jordan, M.I., Bradley, P., Das, R., Kim, D.E.: Feature selection methods for improving protein structure prediction with rosetta. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems, pp. 137–144. Curran Associates, Inc., Vancouver, British Columbia, Canada (2008)

  5. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1, 131–156 (1997)

    Article  Google Scholar 

  6. Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)

    Article  Google Scholar 

  7. Giovannetti, V., Lloyd, S., Maccone, L.: Quantum private queries. Phys. Rev. Lett. 100(23), 230502 (2008)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  8. Wei, C.Y., Cai, X.Q., Liu, B., Wang, T., Gao, F.: A generic construction of quantum-oblivious-key-transfer-based private query with ideal database security and zero failure. IEEE Trans. Comput. 67, 2–8 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  9. Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemp. Phys. 56, 172–185 (2015)

    Article  ADS  MATH  Google Scholar 

  10. Li, K., Qiu, D., Li, L., Zheng, S., Rong, Z.: Application of distributed semi-quantum computing model in phase estimation. Inf. Process. Lett. 120, 23–29 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  11. Yu, C.H., Gao, F., Wang, Q.L., Wen, Q.Y.: Quantum algorithm for association rules mining. Phys. Rev. A 94, 042311 (2016)

    Article  ADS  Google Scholar 

  12. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549, 195 (2017)

    Article  ADS  Google Scholar 

  13. Ruan, Y., Xue, X., Liu, H., Tan, J., Li, X.: Quantum algorithm for k-nearest neighbors classification based on the metric of hamming distance. Int. J. Theor. Phys. 56, 3496–3507 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  14. Ezhov, A.A., Ventura, D.: Quantum neural networks. Future Directions Intell. Syst. Inf. Sci. 45, 213–235 (2000)

    Google Scholar 

  15. Cheng, S., Chen, J., Wang, L.: Quantum entanglement: from quantum states of matter to deep learning. Physics 46(7), 416–423 (2017)

    Google Scholar 

  16. Aimeur, E., Brassard, G., Gambs, S.: Quantum speed-up for unsupervised learning. Mach. Learn. 90, 261–287 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103, 150502 (2009)

    Article  ADS  MathSciNet  Google Scholar 

  18. Weinstein, M., Meirer, F., Hume, A., Sciau, P., Shaked, G., Hofstetter, R., Persi, E., Mehta, A., Horn, D.: Analyzing big data with dynamic quantum clustering. arXiv:1310.2700 (2013)

  19. Dong, D., Chen, C., Chen, Z.: Quantum reinforcement learning. IEEE Trans. Syst. Man Cybern. B 38, 1207–1220 (2008)

    Article  Google Scholar 

  20. Duan, B., Yuan, J., Liu, Y., Li, D.: Quantum algorithm for support matrix machines. Phys. Rev. A 96, 032301 (2017)

    Article  ADS  Google Scholar 

  21. Chen, C., Dong, D., Chen, Z.: Quantum computation for action selection using reinforcement learning. Int. J. Quantum Inf. 4, 1071–1083 (2006)

    Article  MATH  Google Scholar 

  22. Chatterjee, R., Yu, T.: Generalized coherent states, reproducing kernels, and quantum support vector machines. Quantum Inf. Comput. 17, 1292 (2017)

    MathSciNet  Google Scholar 

  23. Adachi, S.H., Henderson, M.P.: Application of quantum annealing to training of deep neural networks. arXiv:1510.06356 (2015)

  24. Wiebe, N., Kapoor, A., Svore, K.M.: Quantum deep learning. arXiv:1412.3489 (2014)

  25. Pan, J., Cao, Y., Yao, X., Li, Z., Ju, C., Chen, H., Peng, X., Kais, S., Du, J.: Experimental realization of quantum algorithm for solving linear systems of equations. Phys. Rev. A 89, 022313 (2014)

    Article  ADS  Google Scholar 

  26. Cai, X.D., Wu, D., Su, Z.E., Chen, M.C., Wang, X.L., Li, L., Liu, N.L., Lu, C.Y., Pan, J.W.: Entanglement-based machine learning on a quantum computer. Phys. Rev. Lett. 114, 110504 (2015)

    Article  ADS  Google Scholar 

  27. Chen, J., Wang, L., Charbon, E.: A quantum-implementable neural network model. Quantum Inf. Process. 16, 245 (2017)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  28. Lu, S., Braunstein, S.L.: Quantum decision tree classifier. Quantum Inf. Process. 13, 757–770 (2014)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  29. Zhang, G., Hu, L., Jin, W.: Resemblance coefficient and a quantum genetic algorithm for feature selection. In: International Conference on Discovery Science, pp. 155–168. Springer (2004)

  30. Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113, 130503 (2014)

    Article  ADS  Google Scholar 

  31. Li, Z., Liu, X., Xu, N., Du, J.: Experimental realization of a quantum support vector machine. Phys. Rev. Lett. 114(14), 140504 (2015)

    Article  ADS  Google Scholar 

  32. Liu, Y., Zhang, S.: Fast quantum algorithms for least squares regression and statistic leverage scores. In: International Workshop on Frontiers in Algorithmics, pp. 204–216. Springer (2015)

  33. Doquire, G., Verleysen, M.: Mutual information-based feature selection for multilabel classification. Neurocomputing 122, 148–155 (2013)

    Article  MATH  Google Scholar 

  34. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)

    Article  MATH  Google Scholar 

  35. Maldonado, S., Weber, R.: A wrapper method for feature selection using support vector machines. Inf. Sci. 179, 2208–2217 (2009)

    Article  Google Scholar 

  36. Wang, A., An, N., Chen, G., Li, L., Alterovitz, G.: Accelerating wrapper-based feature selection with k-nearest-neighbor. Knowl. Based Syst. 83, 81–91 (2015)

    Article  Google Scholar 

  37. Apolloni, J., Leguizamón, G., Alba, E.: Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments. Appl. Soft Comput. 38, 922–932 (2016)

    Article  Google Scholar 

  38. Mao, K.Z.: Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Trans. Syst. Man Cybern. B (Cybernetics) 34, 629–634 (2004)

    Article  Google Scholar 

  39. Hu, Y., Lan, W., Miller, D.: Handling high-dimension (high-feature) microrna data. In: Bioinformatics in MicroRNA Research. Methods in Molecular Biology, vol. 1617, pp. 179–186 (2017)

  40. Durr, C., Hoyer, P.: A quantum algorithm for finding the minimum. arXiv:quant-ph/9607014 (1996)

  41. Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10, 631–633 (2014)

    Article  Google Scholar 

  42. Bennett, C.H.: Logical reversibility of computation. IBM J. Res. Dev. 17, 525–532 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  43. Boyer, M., Brassard, G., Høyer, P., Tapp, A.: Tight bounds on quantum searching. arXiv:quant-ph/9605034 (1996)

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472452, 61772565, 61602116, 61502179), the Natural Science Foundation of Guangdong Province of China (Grant No. 2017A030313378), the Science and Technology Program of Guangzhou City of China (No. 201707010194), the Fundamental Research Funds for the Central Universities (No. 17lgzd29) and the Research Foundation for Talented Scholars of Foshan University (No. gg040996).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China

    Zhimin He

  2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Lvzhou Li

  3. School of Economics and Management, Wuyi University, Jiangmen, 529020, China

    Zhiming Huang

  4. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Haozhen Situ

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  1. Zhimin He
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Correspondence to Haozhen Situ.

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He, Z., Li, L., Huang, Z. et al. Quantum-enhanced feature selection with forward selection and backward elimination. Quantum Inf Process 17, 154 (2018). https://doi.org/10.1007/s11128-018-1924-8

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