TU Darmstadt / ULB / TUbiblio

Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis

Weber, Patrick and Weber, Nicolas and Goesele, Michael and Kabst, Rüdiger (2017):
Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis.
In: Social Science Computer Review, SAGE Publications Inc, ISSN 0894-4393, DOI: 10.1177/0894439317725836, [Online-Edition: http://journals.sagepub.com/doi/10.1177/0894439317725836],
[Article]

Abstract

Policy making depends on good knowledge of the corresponding target audience. To maximize the designated outcome, it is essential to understand the underlying coherences. Machine learning techniques are capable of analyzing data containing behavioral aspects, evaluations, attitudes, and social values. We show how existing machine learning techniques can be used to identify behavioral aspects of human decision-making and to predict human behavior. These techniques allow to extract high resolution decision functions that enable to draw conclusions on human behavior. Our focus is on voter turnout, for which we use data acquired by the European Social Survey on the German national vote. We show how to train an artificial expert and how to extract the behavioral aspects to build optimized policies. Our method achieves an increase in adjusted R² of 102% compared to a classic logistic regression prediction. We further evaluate the performance of our method compared to other machine learning techniques such as support vector machines and random forests. The results show that it is possible to better understand unknown variable relationships.

Item Type: Article
Erschienen: 2017
Creators: Weber, Patrick and Weber, Nicolas and Goesele, Michael and Kabst, Rüdiger
Title: Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis
Language: English
Abstract:

Policy making depends on good knowledge of the corresponding target audience. To maximize the designated outcome, it is essential to understand the underlying coherences. Machine learning techniques are capable of analyzing data containing behavioral aspects, evaluations, attitudes, and social values. We show how existing machine learning techniques can be used to identify behavioral aspects of human decision-making and to predict human behavior. These techniques allow to extract high resolution decision functions that enable to draw conclusions on human behavior. Our focus is on voter turnout, for which we use data acquired by the European Social Survey on the German national vote. We show how to train an artificial expert and how to extract the behavioral aspects to build optimized policies. Our method achieves an increase in adjusted R² of 102% compared to a classic logistic regression prediction. We further evaluate the performance of our method compared to other machine learning techniques such as support vector machines and random forests. The results show that it is possible to better understand unknown variable relationships.

Journal or Publication Title: Social Science Computer Review
Publisher: SAGE Publications Inc
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Graphics, Capture and Massively Parallel Computing
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Exzellenzinitiative > Graduate Schools
Exzellenzinitiative
Date Deposited: 13 Sep 2017 11:38
DOI: 10.1177/0894439317725836
Official URL: http://journals.sagepub.com/doi/10.1177/0894439317725836
Identification Number: doi:10.1177/0894439317725836
Related URLs:
Export:

Optionen (nur für Redakteure)

View Item View Item