In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

ABSTRACT
1. INTRODUCTION
2. DATA AND METHOD
    2.1 Data
    2.2 Support Vector Machine
    2.3 Statistical Verification Method
3. RESULTS
4. SUMMARY AND CONCLUSION
REFERENCES

• SEONGHWAN CHOI(Korea Astronomy and Space Science Institute, Department of Astronomy and Space Science, Kyung Hee University)
• YONG-JAE MOON(Department of Astronomy and Space Science, Kyung Hee University) Corresponding Author
• NGO ANH VIEN(Institute for Artificial Intelligence, Ravensburg-Weingarten University of Applied Sciences)
• YOUNG-DEUK PARK(Korea Astronomy and Space Science Institute)