计算机应用研究2012，Vol.29Issue(5)：1787-1790,4.DOI:10.3969/j.issn.1001-3695.2012.05.049
基于小生境遗传算法的分类优化方法
Classifier optimization method using niche genetic algorithm
摘要
Abstract
According to multi-classify problem, the multi-classes classifier constructed by binary classes classifier are usually very slow to be trained. When a large number of categories of data are to be classified, the training work could be very difficult. Hyper-sphere support vector machine (HSSVM) can be extended to solve this multi-classification problem. Each category data trains only one HSSVM, the sample space is divided by multiple optimal hyper-spheres. In order to improve the performance of classifier, this paper used improved crowding niche genetic algorithm (ICNGA) to select features, chose optimal feature subset for different target classes. Using UCI data set of numerical experiment shows that the classifiers have a higher accuracy if ICNGA has been used for feature selection, especially the sample data has a large number of categories or feature vectors.关键词
遗传算法/排挤小生境技术/超球支持向量机/特征选择Key words
genetic algorithm(GA)/crowding niche technology/hyper-sphere support vector machine(HSSVM)/feature selection分类
信息技术与安全科学引用本文复制引用
李隽颖,楼晓俊..基于小生境遗传算法的分类优化方法[J].计算机应用研究,2012,29(5):1787-1790,4.基金项目
国家重点基础研究发展计划资助项目(2011CB302906) （2011CB302906）
国家重大科技专项基金资助项目(2010ZX03006-004) （2010ZX03006-004）
