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Paper ThPSBT1.41

Semenovich, Dimitri (UNSW), Sowmya, Arcot (University of New South Wales), Goldsmith, Benjamin E (University of Sydney)

Predicting Onsets of Genocide with Sparse Additive Models

Scheduled for presentation during the Regular Session "Poster Shotgun (13): PR" (ThPSBT1), Thursday, November 15, 2012, 14:00−14:30, Main Hall

21st International Conference on Pattern Recognition, November 11-15, 2012, Tsukuba International Congress Center, Tsukuba, Japan

This information is tentative and subject to change. Compiled on April 20, 2014

Keywords Machine Learning and Data Mining, Pattern Recognition for Art, Cultural Heritage and Entertainment

Abstract

Prevention of genocide is one of the most impor- tant 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.

 

 
  
 

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