Weber, Patrick ; Weber, Nicolas ; Goesele, Michael ; Kabst, Rüdiger (2017)
Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis.
In: Social Science Computer Review, 35
doi: 10.1177/0894439317725836
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Weber, Patrick ; Weber, Nicolas ; Goesele, Michael ; Kabst, Rüdiger |
| Art des Eintrags: | Bibliographie |
| Titel: | Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis |
| Sprache: | Englisch |
| Publikationsjahr: | 12 September 2017 |
| Verlag: | SAGE Publications Inc |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Social Science Computer Review |
| Jahrgang/Volume einer Zeitschrift: | 35 |
| DOI: | 10.1177/0894439317725836 |
| URL / URN: | http://journals.sagepub.com/doi/10.1177/0894439317725836 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphics, Capture and Massively Parallel Computing Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 13 Sep 2017 11:38 |
| Letzte Änderung: | 09 Dez 2021 11:45 |
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