支持向量数据描述在西北暴雨预报中的应用试验  

Support Vector Data Description in Rainstorm Prediction of the Northwest China

作　　者：燕东渭[1] 孙田文[1] 杨艳[1] 方建刚[1] 刘志镜[2] 

机构地区：[1]陕西省气象局,西安710015 [2]西安电子科技大学,西安710077

出　　处：《应用气象学报》2007年第5期676-681,共6页Journal of Applied Meteorological Science

基　　金：陕西省科技计划项目(2001K092G7)资助

摘　　要：传统机器学习中通常隐含假设所研究问题是类别平衡的,气象预报中预测灾害天气时就不满足这个假设,这时往往需要预测重要而稀少的正类(少数类)。传统机器学习以精度最大化为目标,在遇到不平衡类别问题时,容易训练出把所有实例都分为反类(多数类)的平庸的分类器。支持向量数据描述是从支持向量机(SVM)发展而来的基于核的机器学习方法,只使用一类样本就可以工作,适合于不平衡类别。以铜川暴雨预测作为试验对象,对SVM和支持向量数据描述(SVDD)进行了对比试验。试验结果表明对于这个不平衡类别问题SVDD具有优势。The expert system（ES） has been studied and applied in meteorological field widely, ES depends on knowledge engineers to enter knowledge used in inferring by computer, which is toilsome and error-prone work. As another branch of artificial intelligenee（AI）, machine learning aims at solving the knowledge obtaining problem automatically and paving a path to remedy the shortcoming of ES, But machine learning still does not work well if it is not tailored to fit characteristics of weather foresting, among which imbalanced class is an important problem deserving study. Although it is usually assumed implicitly by the machine learning research community that the classes are well-balanced, there exist many domains for which one class is represented by a large number of examples while the other is represented by only a few, and there are many applications demanding to classify important but rare positive examples （minority）, It is a typical example of learning from imbalaneed training set to predict such disaster weathers as hail and rainstorm in meteorology, Though they are small probability events, those disastrous weathers will bring about serious destruction. Thus disastrous weathers＇ prediction has been paid much more attention by meteorologist than normal weather prediction. Normally, the number of examples belonging to normal weather is much more than disaster ones, Aiming at improving the accuracy, trivial classifier that labels every example with majority when faced with imbalaneed class distribution will be lead to by traditional machine learning algorithms, By doing so, high accuracy would be obtained. Imbalaneed class is a stumbling block stymieing practical attempts to apply machine learning to realistic problem, In order to find algorithms being resistant to imbalanced class distribution, threat score （TS） is used as criterion to evaluate classifiers, As a kernel snethod, SVM fails to deal with imbalanced class problem too although based on statistical learning theory, and working well

关 键 词：机器学习 支持向量数据描述(SVDD) 支持向量机(SVM) 暴雨预测 

分 类 号：P457.6[天文地球—大气科学及气象学]