To remove some redundant or irrelevant features in multi-source multi-label decision system, a feature selection algorithm based on positive region for multi-source multi-label data is explored, which uses the feature dependency carried on the fusion decision table. Finally, examples are introduced ...
将这两者结合起来,就形成了多标签信息驱动特征选择(Multi-label Informed Feature Selection)。 多标签问题简介: 传统的分类问题通常是单标签分类,即每个样本只能属于一个类别。而在现实场景中,很多问题都是多标签问题,一个样本可能属于多个不同的类别。例如,在图像分类中,一张图像可能同时包含猫和树,而不是仅仅属于...
multi-label data are obtained based on granular computing; second, the feature complementarity is estimated based on neighborhood mutual information without ... W Qian,X Long,Y Wang,... - 《Applied Soft Computing》 被引量: 0发表: 2020年 Multilabel feature selection using ML-ReliefF and neighb...
Multi-label feature selection attracts considerable attention from multi-label learning. Information theory-based multi-label feature selection methods intend to select the most informative features and reduce the uncertain amount of information of labels. Previous methods regard the uncertain amount of info...
multi-label naive Bayes, is proposed. In order to improve its performance, a two-stage filter-wrapper feature selection strategy is also incorporated. Specifically, in the first stage, feature extraction techniques based on principle component analysis (PCA) are used to eliminate irrelevant and ...
Feature selection, as an important pre-processing technique, can efficiently mitigate the issue of “the curse of dimensionality” by selecting discriminative features especially for multi-label learning, a discriminative feature subset can improve the classification accuracy. The existing feature selection ...
Multi-LabelInformedFeatureSelection 系统标签: labelfeaturemultiselectioninformedmifs Multi-LabelInformedFeatureSelectionLingJian1,2∗,JundongLi1∗,KaiShu1,HuanLiu11.ComputerScienceandEngineering,ArizonaStateUniversity,Tempe,85281,USA2.CollegeofScience,ChinaUniversityofPetroleum,Qingdao,266555,China{ling.jian,ju...
public final class ImageClassificationMultilabel extends AutoMLVerticalImage Classification Multilabel. Multi-label image classification is used when an image could have one or more labels from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'....
represented by(x_1,y_1),...(x_n,y_n)where(x,y)\in {\mathscr {X}}\times \{1,2...M\},the featurex \in \mathop {{\mathbb {R}}}^dis sampled from input space{\mathscr {X}}and labely \in \{1,2,..M\}. In the absence of source training data, we leverage the ...
import make_multilabel_classification # 这会生成一个随机多标签数据集 X, y = make_multilabel_...