Predicting onsets of genocide with sparse additive models

Author

Semenovich, D. ; Sowmya, Arcot ; Goldsmith, B.E.

Author_Institution

Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Sydney, NSW, Australia

fYear

2012

fDate

11-15 Nov. 2012

Firstpage

3549

Lastpage

3552

Abstract

Prevention of genocide is one of the most important challenges before the international community. In this paper we apply recent machine learning techniques to forecast the onset of political instability and genocide. Specifically, we employ sparse additive models which are both flexible and maintain interpretability of the results. Our model demonstrates a reasonable degree of forecasting performance over the hold-out period 1988-2003.

Keywords

ethical aspects; forecasting theory; learning (artificial intelligence); politics; flexible interpretability; genocide; hold-out period; international community; interpretability maintenance; machine learning techniques; onset political instability forecasting; onsets prediction; sparse additive models; Additives; Casting; Educational institutions; Forecasting; Logistics; Predictive models; Standards;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition (ICPR), 2012 21st International Conference on

Conference_Location

Tsukuba

ISSN

1051-4651

Print_ISBN

978-1-4673-2216-4

Type

conf

Filename

6460931

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=595489