Influences of misprediction costs on solar flare prediction  

作　　者：HUANG Xin WANG HuaNing DAI XingHua 

机构地区：[1]Key Laboratory of Solar Activity,National Astronomical Observatories of Chinese Academy of Sciences,Beijing 100012,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2012年第10期1956-1962,共7页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the Young Researcher Grant of National Astronomical Observatories,Chinese Academy of Sciences;the National Basic Research Program of China (Grant No.2011CB811406);the National Natural Science Foundation of China(Grant Nos.10733020,10921303 and 11078010)

摘　　要：The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction.Hence,solar flare prediction is considered a cost sensitive problem.A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm.Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region.These selected parameters are applied as the inputs of the solar flare prediction model.The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares.It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples,and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares.This can be considered as the guide line for choosing proper cost to meet the requirements in different applications.The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction. Hence, solar flare prediction is considered a cost sensitive problem. A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm. Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region. These selected parameters are applied as the inputs of the solar flare prediction model. The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares. It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples, and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares. This can be considered as the guide line for choosing proper cost to meet the requirements in different applications.

关 键 词：flares： forecasting sun： magnetic field cost sensitive learning 

分 类 号：TP317[自动化与计算机技术—计算机软件与理论] P182.52[自动化与计算机技术—计算机科学与技术]