基于ReliefF和SBS的赤潮生物图像特征选择方法研究  

Research on Red Tide Algae Images Feature Selection Method Based on ReliefF and SBS

作　　者：李淑芳[1] 

机构地区：[1]中国环境管理干部学院,河北秦皇岛066004

出　　处：《科技广场》2014年第5期12-15,共4页Science Mosaic

基　　金：河北省教育厅资助科研项目(编号:Z2013028)

摘　　要：针对赤潮生物提出具有较高准确率的实时自动分类方法,本文提出采用ReliefF-SBS进行特征选择,即针对赤潮生物图像原始数据集进行特征分析,并在此基础上,对原始特征集进行特征选择以去除特征集中的无关特征和冗余特征,得到最优特征子集,减少它们对分类器分类精度的影响。文中给出了实验结果和分析,同时验证了对k-Nearest Neighbor algorithm(KNN)和Support Vector Machine(SVM)分类器分类效果的影响。In order to put forward a real-time automatic classification method with high accuracy aimed at red tide algae, this paper proposes to use RetiefF-SBS for feature selection. Namely, we carry out feature analysis about red tide algae image original data set. And on this basis, we make feature selection to remove irrelevant features and redundant features from the original feature set, to get the optimal feature subset, and reduce their impact on the clas- sification accuracy. The paper also presents the results and the analysis, and meanwhile verifies the influences on two feature selection classifiers of k-Nearest Neighbor algorithm （KNN）and Support Vector Machine （SVM）.

关 键 词：图像特征选择 RELIEFF SBS 赤潮生物图像 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]