摘要
特征选择是模式分类中重要的数据处理方法.文中提出一种基于知识引导微粒群优化的特征选择方法.该方法采用特征被选择的概率对微粒进行编码,将包含离散变量的特征选择问题转化为一类连续变量优化问题.依据微粒适应值的大小及微粒分量被选择的频率,确定特征所属的类型及其被更新的概率,以加快微粒群收敛的速度.将所提方法应用于10个典型测试数据集及肝炎病临床诊断数据集,实验结果表明,该方法在减少特征个数的前提下,能够提高分类的精度.
Feature ＂selection is one of important data processing methods in pattern classification. A method of selecting features is proposed based on knowledge guided particle swarm optimization. The problem of feature selection which contains discrete variables is converted to an optimization one with continuous variables by encoding the particles with the selected probabilities of features. The type and its updated probability of the feature are determined by the particle＇ s fitness and the selection frequency of＇ the particle component in order to speed up the convergence of the swarm. The experimental results on 10 typical test datasets and a clinical diagnosis dataset of hepatitis show that the proposed method improves the classification accuracy on the premise of reducing the number of features.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第1期1-10,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61005089)
江苏省自然科学基金项目(No.BK2011215)
高等学校博士学科点专项科研基金项目(No.20100095120016)
中国博士后科学基金项目(No.2012M521142)资助
关键词
微粒群优化
特征选择
特征划分
疾病诊断
Particle Swarm Optimization, Feature Selection, Feature Division, Disease Diagnosis
