The Sample Selection Model Based on Improved Autoencoder for the Online Questionnaire Investigation

  • Yijie Pang
  • Shaochun Wu
  • Honghao Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)


This paper presents the sample selection model based on improved autoencoder to solve low response rate in the online questionnaire investigation industry. This model utilizes the improved autoencoder to extract the samples’ features and uses the softmax classifier to predict the samples’ loyalty. Furthermore, the autoencoder is improved with three steps: first, the number of middle hidden layer nodes is determined by Singular Value Decomposition (SVD); second, the loss function of the autoencoder is improved with the information gain ratio; finally, the concept of Random Denoising Autoencoder (RDA) is introduced to enhance the robustness of the model. Through the selection model, samples with high loyalty will be picked out to answer the questionnaire so that the response rate can be improved. Experiments are performed to determine the feasibility and effectiveness of the model. Compared with the BP neural networks, the prediction accuracy of our model is totally improved about 8.5% and the success rate of sending questionnaires is also improved about 15%.


Online questionnaire investigation Response rate Improved autoencoder Sample selection model 



This study is funded in part by a Xinjiang Social Science Foundation (No. 2015BGL100). We also would like to thank all anonymous reviewers for your insightful comments and useful suggestions.


  1. 1.
    Ishii, Y., Takeyasu, H., Takeyasu, D., Takeyasu, K.: Multivariate analysis on a questionnaire investigation for the rare sugars. In: Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems, pp. 214–219. IEEE Press, Sapporo (2016)Google Scholar
  2. 2.
    LaRose, R., Tsai, H.Y.S.: Completion rates and non-response error in online surveys: comparing sweepstakes and pre-paid cash incentives in studies of online behavior. Comput. Hum. Behav. 34, 110–119 (2014)CrossRefGoogle Scholar
  3. 3.
    Kaplowitz, M.D., Hadlock, T.D., Levine, R.: A comparison of web and mail survey response rates. Public. Opin. Quart. 68(1), 94–101 (2004)CrossRefGoogle Scholar
  4. 4.
    Sivo, S.A., Saunders, C., Chang, Q., Jiang, J.J.: How low should you go? Low response rates and the validity of inference in IS questionnaire research. J. Assoc. Inf. Syst. 7(6), 17 (2006)Google Scholar
  5. 5.
    Fang, J., Shao, P., Lan, G.: Effects of innovativeness and trust on web survey participation. Comput. Hum. Behav. 25(1), 144–152 (2009)CrossRefGoogle Scholar
  6. 6.
    Chien, Y.T., Chang, C.Y.: Exploring the feasibility of an online contextualised animation-based questionnaire for educational survey. Brit. J. Educ. Technol. 41(5) (2010)Google Scholar
  7. 7.
    Baruch, Y., Holtom, B.C.: Survey response rate levels and trends in organizational research. Hum. Relat. 61(8), 1139–1160 (2008)CrossRefGoogle Scholar
  8. 8.
    Fan, W., Yan, Z.: Factors affecting response rates of the web survey: a systematic review. Comput. Hum. Behav. 26(2), 132–139 (2010)CrossRefGoogle Scholar
  9. 9.
    Sali, R., Roohafza, H., Sadeghi, M., Andalib, E., Shavandi, H., Sarrafzadegan, N.: Validation of the revised stressful life event questionnaire using a hybrid model of genetic algorithm and artificial neural networks. Comput. Math. Method. M. 2013 (2013)Google Scholar
  10. 10.
    Kashima, K.: Nonlinear model reduction by deep autoencoder of noise response data. In: 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 5750–5755. IEEE Press, Las Vegas (2016)Google Scholar
  11. 11.
    Kamyshanska, H., Memisevic, R.: The potential energy of an autoencoder. IEEE Trans. Pattern Anal. 37(6), 1261–1273 (2015)CrossRefGoogle Scholar
  12. 12.
    Yumer, M.E., Asente, P., Mech, R., Kara, L.B.: Procedural modeling using autoencoder networks. In: Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, pp. 109–118. ACM Press, Charlotte (2015)Google Scholar
  13. 13.
    Hojabri, H., Mokhtari, H., Chang, L.: A generalized technique of modeling, analysis, and control of a matrix converter using SVD. IEEE Trans. Ind. Elect. 58(3), 949–959 (2011)CrossRefGoogle Scholar
  14. 14.
    Ng, A.: Sparse autoencoder. CS294A Lect. Notes. 72(2011), 1–19 (2011)Google Scholar
  15. 15.
    Yang, Q., Zhou, Y., Yu, Y., Yuan, J., Xing, X., Du, S.: Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing. J. Supercomput. 71(8), 3037–3053 (2015)CrossRefGoogle Scholar
  16. 16.
    Lu, X., Tsao, Y., Matsuda, S., Hori, C.: Speech enhancement based on deep denoising autoencoder. In: 14th Annual Conference of the International Speech Communication Association, pp. 436–440. ISCA Press, Lyon (2013)Google Scholar
  17. 17.
    Boureau, Y.L., Cun, Y.L.: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, pp. 1185–1192. NIPS Press, Vancouver (2008)Google Scholar
  18. 18.
    Tao, S., Zhang, T., Yang, J., Wang, X., Lu, W.: Bearing fault diagnosis method based on stacked autoencoder and softmax regression. In: Control Conference (CCC) and 2015 34th Chinese, pp. 6331–6335. IEEE Press, Hangzhou (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

Personalised recommendations