In this paper, we propose an improved biased SVM with weighted within-class structure for imbalanced classification. The new algorithm makes the minority class more clustered by assigning a small weight for the within-class scatter matrix of minority class, which can improve the classification ...
针对不平衡数据集的分类问题,本文利用支持向量机推广能力强的优良特性,提出了smote(syntheticminorityover-samplingtechnique,smote)和biased-svm(biasedsupportvectormachine,biased-svm)相结合的方法。该方法首先对原始数据使用biased-svm方法,然后对求出的支持向量使用smote向上采样方法进行采样,最后再使用biased-svm方法进行...
针对非平衡数据的半监督分类问题,提出了一种基于Biased-SVM的非平衡半监督分类算法.该方法首先利用初始的标记样本集训练处理不平衡数据的Biased-SVM模型,然后用调练好的Biased-SVM模型为未标记样本加上标签,再把新标记样本加入到初始标记样本集中,重新训练Biased-SVM模型,最后在测试集上进行测试... 查看全部>> ...
"In-Depth Comparisons of MaxEnt, Biased SVM and One-Class SVM for One-Class Classification of Remote Sensing Data." Remote Sensing Letters 8 (3): 290-299. doi:10.1080/2150704X.2016.1265689.B. Mack, and B. Waske, "In-depth comparisons of MaxEnt, biased SVM and one-class SVM for one-...
为了直接从H.264码流中检测镜头边界,提出了利用H.264压缩域多特征和Biased—SVM(不平衡支持向量机)分类算法的检测方法。分析帧类型、宏块类型、运动矢量、帧内预测模式等信息,以获得发生镜头突变和渐变的特征。针对镜头边界帧的数量远少于视频帧总数的特点,用Biased—SVM分类方法将视频帧分为突变帧、渐变帧和非镜头...
为了直接从H.264码流中检测镜头边界,提出了利用H.264压缩域多特征和Biased-SVM(不平衡支持向量机)分类算法的检测方法。分析帧类型、宏块类型、运动矢量、帧内预测模式等信息,以获得发生镜头突变和渐变的特征。针对镜头边界帧的数量远少于视频帧总数的特点,用Biased-SVM分类方法将视频帧分为突变帧、渐变帧和非镜头边界...
G-SMOTEBiased-SVM行为日志网络安全针对目前内部威胁用户检测召回率低及数据类别不平衡的问题,提出一种基于Geometric SMOTE(G-SMOTE)和Biased-SVM的内部威胁用户检测方法.该方法对用户行为进行特征提取,利用G-SMOTE算法在每个威胁用户样本中心定义一个几何区域用于生成威胁用户样本,保证了训练集中的正常用户,威胁用户的类别...
L-infinity - BSVM includes the following merits: (1) it allows all sample points to participate in learning to prompt classification performance, especially in the case where the size of labeled data is small; (2) it minimizes the distance of the sample points that are (outliers in Non-i...
He, Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC, J. Mol. Graph. Model. 76 (2017) 356-363.Ju, Z.; He, J.J. Prediction of lysine propionylation sites using biased svm and incorporating four different sequence...
Mapped prediction results of binary and biased SVM models for the three focal species.Claire, A. BaldeckGregory, P. AsnerRobin, E. MartinChristopher B., AndersonDavid, E. KnappJames, R. KellnerS., Joseph Wright