Searching for Interpretable Demographic Patterns

Anna Muratova, Robiul Islam, Ekaterina Mitrofanova and Dmitry Ignatov ()

MPRA Paper from University Library of Munich, Germany

Abstract: Nowadays there is a large amount of demographic data which should be analyzed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of data mining. Two kinds of experiments are considered in this work: 1) generation of additional secondary features from events and evaluation of its influence on accuracy; 2) exploration of features influence on classification result using SHAP (SHapley Additive exPlanations). An algorithm for creating secondary features is proposed and applied to the dataset. The classifications were made by two methods, SVM and neural networks, and the results were evaluated. The impact of events and features on the classification results was evaluated using SHAP; it was demonstrated how to tune model for improving accuracy based on the obtained values. Applying convolutional neural network for sequences of events allowed improve classification accuracy and surpass the previous best result on the studied demographic dataset.

Keywords: data mining; demographics; neural networks; classification; SHAP; interpretation (search for similar items in EconPapers)
JEL-codes: C02 C15 I00 J13 (search for similar items in EconPapers)
Date: 2019-09-23, Revised 2019-09-23
New Economics Papers: this item is included in nep-big, nep-cmp and nep-gth
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/97305/1/paper2.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:97305

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().