摘要
使用模糊积分的方法将多个分类器进行融合可以提高分类精度,但是如何得到最优的模糊测度是一个尚未解决的问题。本文根据模糊测度Sugeno积分的理想特性,用模糊测度代替各个分类器的权值,利用粒子群算法全局搜索的优势,将模糊测度对应于粒子,并随速度和位置并不断调整,从而得到全局最优的模糊测度。通过仿真实例验证了新的多分类器融合模型具有较低的分类错误率,并能有效地提高分类精度。
Combination of many different classifiers can improve classification accuracy.Fuzzy integral is one of the ways to combineclassifiers,but how to get the best fuzzy integral measure is a problem which remains unsolved.Considering the perfect characteristicof the Sugeno integral,this paper takes the fuzzy integral measure replacing the weight of the single classifiers.Based the advantageon global search of the PSO,the fuzzy integral measure is adjusted along with the increase of the velocity and location of the parti-cle,the best global fuzzy integral measure is found finally.The experimental results show that this new model efficiently improve theclassifier accuracy.
出处
《微计算机信息》
2010年第9期205-207,共3页
Control & Automation
基金
基金颁发部门:湖南省自然科学基金资助项目(07JJ3120)
