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Paper Abstract and Keywords
Presentation 2020-12-16 13:35
A Consideration on Detecting Anormal Respondents in Large Questionnaire Response Data
Hiroyuki Takahashi, Wataru Kameyama, Mutsumi Suganuma (Waseda Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In a questionnaire with a variety of questions for consumers to answer, there may be a small number of specific answers that are far-off from the distribution and different from most of the respondents. These anormal responses have a significant impact on the results. However, in a large-scale questionnaire survey, it is generally difficult to identify such respondents individually. Therefore, in this paper, we report a result of consideration of anormal respondent detection in the feature space using variational autoencoder as a dimension reduction method. The large-scale questionnaire response data of approximately 1,000 questions (approximately 46,000 including sub-questions), which was conducted for approximately 15,000 people, are divided into question categories. And focusing on the answers that are deviated from the majority of others in each of those categories, we detect the respondents who gave anormal answers in each question category. As a result, we find multiple cases where a person detected as an anormal respondent in one question category is also detected as an anormal respondent in another question category. Therefore, it is inferred that these respondents should be excluded from the aggregated results.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomaly Detection / Autoencoder / Dimension Reduction / PCA / t-SNE / Big Data / /  
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Conference Information
Committee HCGSYMPO  
Conference Date 2020-12-15 - 2020-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
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Paper Information
Registration To HCGSYMPO 
Conference Code 2020-12-HCGSYMPO 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Consideration on Detecting Anormal Respondents in Large Questionnaire Response Data 
Sub Title (in English)  
Keyword(1) Anomaly Detection  
Keyword(2) Autoencoder  
Keyword(3) Dimension Reduction  
Keyword(4) PCA  
Keyword(5) t-SNE  
Keyword(6) Big Data  
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Keyword(8)  
1st Author's Name Hiroyuki Takahashi  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Wataru Kameyama  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Mutsumi Suganuma  
3rd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2020-12-16 13:35:00 
Presentation Time 15 minutes 
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