This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of
multiple indicators that have already been successfully used in different seismic zones by the application of feature
selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in
terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones
(the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make
the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing
the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are
reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are
significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection
techniques for improving earthquake prediction methods. So, the infor-mation gain of different seismic indicators has been
determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized
prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula
have been charac-terized by means of an information gain analysis obtained from different seismic indicators. The results
confirm the methodology proposed as the best features in terms of information gain are the same for both regions.Ministerio de Ciencia y Tecnología BIA2004-01302Ministerio de Ciencia y Tecnología TIN2011-28956-C02-01Junta de Andalucía P11-TIC-752