计算机工程与应用Issue(14)：148-151,167,5.DOI:10.3778/j.issn.1002-8331.1212-0372
改进K近邻和支持向量机相融合的天气识别
Weather identification based on improved K nearest neighbor and sup-port vector machine
摘要
Abstract
The weather which is affected by many factors is changeable and uncertain, single model is difficult to obtain high identification rate, therefore, this paper proposes a weather identification model(IKNN-SVM)based on improved K nearest neighbor and support vector machine. Firstly, the distance between of the testing sample and a hyper plane is cal-culated, then the distance is compared with the threshold, if distance is greater than the threshold, then support vector machine is used to identify the weather, otherwise the K nearest neighbor algorithm is used to identify the weather, and the sample density is introduced to solve the defects of K nearest neighbor algorithm, finally the simulation experiment is car-ried out to test on the performance of model. The simulation results show that, compared with the single KNN or SVM, IKNN-SVM has improved weather identification correct rate and can overcome the defects of the single model.关键词
天气识别/支持向量机/K近邻/识别正确率Key words
weather identification/support vector machine/K nearest neighbor/recognition correct rate分类
信息技术与安全科学引用本文复制引用
张红艳,李茵茵,万伟..改进K近邻和支持向量机相融合的天气识别[J].计算机工程与应用,2014,(14):148-151,167,5.基金项目
广东省气象局气象科技项目（No.2011B03，No.201007）。 （）
