Black-Box Classification Techniques for Demographic Sequences: from Customised SVM to RNN

Anna Muratova, Pavel Sushko and Thomas H. Espy

MPRA Paper from University Library of Munich, Germany

Abstract: Nowadays there is a large amount of demographic data which should be analysed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of data mining. The aim of this study is to compare the methods of classification of demographic data by customising the SVM kernels using various similarity measures. Since demographers are interested in sequences without discontinuity, formulas for such sequences similarity measures were derived. Then they were used as kernels in the SVM method, which is the novelty of this study. Recurrent neural network algorithms, such as Simple RNN, GRU and LSTM, are also compared. The best classification result with SVM method is obtained using a special kernel function in SVM by transforming sequences into features, but recurrent neural network outperforms SVM.

Keywords: data mining; demographics; support vector machines; neural networks; classification; sequences similarity (search for similar items in EconPapers)
JEL-codes: C14 J11 (search for similar items in EconPapers)
Date: 2017-09-17
New Economics Papers: this item is included in nep-big and nep-cmp
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in CEUR Workshop Proceeding Experimental Economics and Machine Learning.1968(2017): pp. 31-40

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