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Leveraging Label-Specific Discriminant Mapping Features for Multi-Label Learning

https://doi.org/10.1145/3319911

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Knowledge Discovery in Multi-label Phenotype Data

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Mining Multi-label Data

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Combining Instance-Based Learning and Logistic Regression for Multilabel Classification

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Efficient Two Stage Voting Architecture for Pairwise Multi-label Classification

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Learning and Inference in Probabilistic Classifier Chains with Beam Search

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Multilabel classification via calibrated label ranking

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Combining instance-based learning and logistic regression for multilabel classification

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Classifier chains for multi-label classification

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Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference

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Statistical topic models for multi-label document classification

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Multi-label learning with label-specific feature reduction

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Multi-label learning with discriminative features for each label

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Selecting label-dependent features for multi-label classification

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Multi-label learning based on label-specific features and local pairwise label correlation

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Learning multi-label scene classification

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ML-KNN: A lazy learning approach to multi-label learning

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Dependent binary relevance models for multi-label classification

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BoosTexter: A Boosting-based System for Text Categorization

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Multiple Comparisons Among Means

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Hierarchical multi-label prediction of gene function

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Multi-label Classification Using Ensembles of Pruned Sets

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Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-label Learning

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Learning Label Specific Features for Multi-label Classification

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Multi-label classification with meta-label-specific features

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Pairwise learning of multilabel classifications with perceptrons

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Joint Feature Selection and Classification for Multilabel Learning

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Random k-Labelsets for Multilabel Classification

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A Review on Multi-Label Learning Algorithms

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Learning Label-Specific Features and Class-Dependent Labels for Multi-Label Classification

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Lift: Multi-Label Learning with Label-Specific Features

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Matrix Completion for Weakly-Supervised Multi-Label Image Classification

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Nonlinear Dimensionality Reduction by Locally Linear Embedding

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A shared-subspace learning framework for multi-label classification

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Multi-label learning by exploiting label dependency

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LIBSVM

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Data clustering

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The meaning and use of the area under a receiver operating characteristic (ROC) curve.

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Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble

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Multi-label Text Categorization with Joint Learning Predictions-as-Features Method

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A Multi-Label Convolutional Neural Network for Automatic Image Annotation

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Statistical Comparisons of Classifiers over Multiple Data Sets

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Multi-label classification using conditional dependency networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multi-label Classification of Hyperspectral Images Based on Label-Specific Feature Fusion

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Label-Specific Multi-label Classification with Entropy Guided Clustering

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Label-specific disentanglement and correlation-guided fusion for multi-label classification

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Joint label-specific features and label correlation for multi-label learning with missing label

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Multi-label classification by formulating label-specific features from simultaneous instance level and feature level

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Bi-directional mapping for multi-label learning of label-specific features

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Learning label-specific features with global and local label correlation for multi-label classification

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An improved MLTSVM using label-specific features with missing labels

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Multi-label learning of missing labels using label-specific features: an embedded packaging method

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Partial multi-label learning via label-specific feature corrections

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Label distribution learning by utilizing common and label-specific feature fusion space

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LIP-MC: Multi-Constraint Label Independent Prediction in label distribution learning

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Group-preserving label-specific feature selection for multi-label learning

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Selective label enhancement for multi-label classification based on three-way decisions

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Two-stage label distribution learning with label-independent prediction based on label-specific features

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Conditional generation model with dual-perspective feature fusion representation for multi-label classification

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Learning common and label-specific features for multi-Label classification with correlation information

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Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition

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$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

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Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations

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Label-Specific Feature Learning for Partial Multi-Label Classification With Positive and Negative Correlation Modeling

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AME-LSIFT: Attention-Aware Multi-Label Ensemble With Label Subset-SpecIfic FeaTures

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Generic Multi-label Annotation via Adaptive Graph and Marginalized Augmentation

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BiLabel-Specific Features for Multi-Label Classification

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Integrating Global and Local Feature Selection for Multi-Label Learning

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Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification

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End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification

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Latent Semantics Encoding for Label Distribution Learning

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Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

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Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition

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