for example the feature names:bc_pandas_frame=load_breast_cancer(return_X_y=False)print("\nfeat...
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 ...
Li L,Liu H,Ma Z,et al.Multi-label feature selectionvia information gain[M].Advanced data mining andapplications.[S.l.]:Springer International Publishing,2014:345-355.Li L, Liu H, Ma Z, Mo Y, Duan Z, Zhou J, Zhao J (2014) Multi-label feature selection via information gain. In: ...
Multi-label learning deals with data associated with a set of labels simultaneously. Like traditional single-label learning, the high-dimensionality of data is a stumbling block for multi-label learning. In this paper, we first introduce the margin of instance to granulate all instances under diffe...
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...
Label distribution Feature selection Rough set Feature dependency Three-way decisions 1. Introduction Traditional machine learning usually classifies an instance into only one label, namely the single-label learning (SLL), which aims to find the most relevant class label d of the unseen instance [12...
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_extraction.textimportTfidfVectorizerdf=pd.read_csv('PubMed Multi Label Text Classification ...
Semi-supervised learning and multi-label learning pose different challenges for feature selection, which is one of the core techniques for dimension reduction, and the exploration of reducing feature space for multi-label learning with incomplete label information is far from satisfactory. Existing featur...
transforming the input vector into feature vector of length di, fSi:X→Rdi where di is the length of the feature vector of source i(2) A classifier hSi:Rdi→RM from the feature vector into the output label, Ysi. This forms the hypothesis function. ...