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

P. 31–40.

A. Muratova, P. Sushko, T. Espy

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 customizing 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 SimpleRNN, 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: neural networks, data mining, support vector machines, classification, demographics, майнинг данных, демографические последовательности, sequences similarity

Publication based on the results of: Разработка и апробация методик анализа демографических последовательностей (2016)

In book

Proceedings of the Fourth Workshop on Experimental Economics and Machine Learning (EEML 2017), Dresden, Germany, September 17-18, 2017

Vol. 1968. , Aachen: CEUR Workshop Proceedings, 2017.