Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting

Details Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting

Jun 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007chjaa...7..441l&link_type=abstract

Chinese Journal of Astronomy and Astrophysics, Volume 7, Issue 3, pp. 441-447 (2007).

Astronomy and Astrophysics

Astrophysics

12

Scientific paper

A method combining the support vector machine (SVM) the K-Nearest Neighbors (KNN), labelled the SVM-KNN method, is used to construct a solar flare forecasting model. Based on a proven relationship between SVM and KNN, the SVM-KNN method improves the SVM algorithm of classification by taking advantage of the KNN algorithm according to the distribution of test samples in a feature space. In our flare forecast study, sunspots and 10 cm radio flux data observed during Solar Cycle 23 are taken as predictors, and whether an M class flare will occur for each active region within two days will be predicted. The SVM-KNN method is compared with the SVM and Neural networks-based method. The test results indicate that the rate of correct predictions from the SVM-KNN method is higher than that from the other two methods. This method shows promise as a practicable future forecasting model.

Affiliated with

Also associated with

No associations

Rating

Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Support Vector Machine combined with K-Nearest Neighbors for Solar Flare Forecasting will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-877468