RT Conference Proceedings
T1 Earthquake Prediction in California Using Feature Selection Techniques
A1 Roiz-Pagador, J.
A1 Chacón Maldonado, Andrés Manuel
A1 Ruiz, R.
A1 Asencio Cortés, Gualberto
K1 Earthquake prediction
K1 California
K1 Feature selection
K1 Search
K1 Evaluation
K1 Classification
K1 Regression
AB Predicting the magnitude of earthquakes is of vital importance and, at the same time, of extreme complexity, where each attribute contributes differently in the process, even introducing noise. Preprocessing using attribute selection techniques helps to alleviate this drawback. In this work, this is demonstrated through an extensive comparison of 47 years of data from the Northern California Earthquake Data Center, where a wide range of feature selection algorithms are applied composed by different search, like population, local and ranking search based; and evaluators, like Correlations, consistency and distance metrics. After that, prediction algorithms will allow to compare the result with and without the application of feature selection, showing that the number of existing attributes can be reduced by 80%, improving metrics of the original, ensuring that the use of attribute selection in this type of problem is quite promising.
PB Springer, Cham
SN 978-3-030-87868-9
SN 978-3-030-87869-6
YR 2021
FD 2021-09-23
LK https://hdl.handle.net/10433/19931
UL https://hdl.handle.net/10433/19931
LA en
NO 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). Advances in Intelligent Systems and Computing, vol 1401
NO Proyectos de investigaciónFECYT -- KERNEL DENSITY ESTIMATION-BASED DATA ANALYTICS
NO Departamento de Deporte e Informática
DS RIO
RD May 24, 2026