摘要
在模式分类系统中,大量无关或冗余的特征往往会降低分类器的性能,因此需要特征选择.本文提出了基于离散微粒群(BPSO)和支持向量机(SVM)封装模式的特征子集选择方法,首先随机产生若干种群(特征子集),然后用BPSO算法对特征进行优化,并用SVM的10阶交叉验证结果指导算法的搜索,最后选出最佳适应度的子集对SVM进行训练.两个UC I机器数据集(户外图像和电离层)的实验结果表明了提出算法的有效性.
In pattern classification system, many irrelevant and redundant features will lessen the performance of classifiers. So it is important to select features. This paper proposed a discrete binary version of particle swarm optimization-support vector machines （BPSO-SVMs） wrapper mode feature selection algorithm. At first, a population of particles （feature subsets） was randomly generated. Then BPSO algorithms searched the feature space guided by the result of SMVs＇ 10-fold crossover validation. After numbers of iteration, the best fitness feature subset was selected out to train the predictor. Experiments on two datasets （ Segmentation and Ionosphere） in UCI machine learning repository confirm the effectiveness of the proposed strategy.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2006年第3期496-498,共3页
Acta Electronica Sinica
关键词
微粒群算法
支持向量机
特征子集选择
particle swarm optimization
support vector machine
feature subset selection
