A comparative study of the use of large margin classifiers on seismic data

Abstract

In this work we present a study on the analysis of a large data set from seismology. A set of different large margin classifiers based on the well-known support vector machine (SVM) algorithm is used to classify the data into two classes based on their magnitude on the Richter scale. Due to the imbalance of nature between the two classes reweighing techniques are used to show the importance of reweighing algorithms. Moreover, we present an incremental algorithm to explore the possibility of predicting the strength of an earthquake with incremental techniques.

Keywords:

• large-scale classification
• SVM
• proximal SVM
• incremental procedure
• real-time problems
• reweighted methods

Acknowledgements

The authors thank the editor and the referees for their valuable comments and suggestions that resulted in improving the quality of presentation of this manuscript. The research of the first author (K.D.) was financially supported by a scholarship awarded by Captain Fanourakis Foundation and State Scholarships Foundation.