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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)

Abstract

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%.

Keywords

Online questionnaire investigation Response rate Improved autoencoder Sample selection model 

Notes

Acknowledgements

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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