Timing, Sequencing, and Quantum of Life Course Events: A Machine Learning Approach |
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Authors: | Francesco C Billari Johannes Fürnkranz Alexia Prskawetz |
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Institution: | (1) Institute of Quantitative Methods, Università Bocconi and IGIER, Viale Isonzo 25, I-20135 Milan, Italy;(2) Department of Computer Science, Knowledge Engineering Group, TU Darmstadt, Hochschulstrasse 10, D-64289 Darmstadt, Germany;(3) Vienna Institute of Demography, Prinz Eugen Strasse 8–10, A 1040 Wien, Austria |
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Abstract: | In this paper we discuss and apply machine learning techniques, using ideas from a core research area in the artificial intelligence
literature to analyse simultaneously timing, sequencing, and quantum of life course events from a comparative perspective.
We outline the need for techniques which allow the adoption of a holistic approach to life course analysis, illustrating the
specific case of the transition to adulthood. We briefly introduce machine learning algorithms to build decision trees and
rule sets and then apply such algorithms to delineate the key features which distinguish Austrian and Italian pathways to
adulthood, using Fertility and Family Survey data. The key role of sequencing and synchronization between events emerges clearly
from the analysis.
Billari F.C., Fürnkranz J., et Prskawetz A., 2006. Calendrier, séquence et intensitédes événements du cycle de vie : une application
des techniques d’apprentissage par machine. Revue Européenne de Démographie, 22: 37–65 |
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Keywords: | data mining event history life course machine learning transition to adulthood |
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