Learning multi-label label-specific features via global and local label correlations

Regularizing extreme learning machine by dual locally linear embedding manifold learning for training multi-label neural network classifiers

(2020) Mohammad Rezaei-Ravari et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Conditional entropy based classifier chains for multi-label classification

(2019) Xie Jun et al.   NEUROCOMPUTING

Multi-label learning with kernel extreme learning machine autoencoder

(2019) Yusheng Cheng et al.   KNOWLEDGE-BASED SYSTEMS

Deep neural network for hierarchical extreme multi-label text classification

(2019) Francesco Gargiulo et al.   APPLIED SOFT COMPUTING

Improving multi-label classification with missing labels by learning label-specific features

(2019) Jun Huang et al.   INFORMATION SCIENCES

Global and local multi-view multi-label learning

(2019) Changming Zhu et al.   NEUROCOMPUTING

Manifold regularized discriminative feature selection for multi-label learning

(2019) Jia Zhang et al.   PATTERN RECOGNITION

A novel approach for learning label correlation with application to feature selection of multi-label data

(2019) Xiaoya Che et al.   INFORMATION SCIENCES

Multilabel Classification With Label-Specific Features and Classifiers: A Coarse- and Fine-Tuned Framework

(2019) Jianghong Ma et al.   IEEE Transactions on Cybernetics

Feature ranking for enhancing boosting-based multi-label text categorization

(2018) Bassam Al-Salemi et al.   EXPERT SYSTEMS WITH APPLICATIONS

Multi-Label Learning with Global and Local Label Correlation

(2018) Yue Zhu et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Multi-label Image Classification with Regional Latent Semantic Dependencies

(2018) Junjie Zhang et al.   IEEE TRANSACTIONS ON MULTIMEDIA

Multi-label learning with label-specific features by resolving label correlations

(2018) Jia Zhang et al.   KNOWLEDGE-BASED SYSTEMS

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

(2018) Wei Weng et al.   NEUROCOMPUTING

SVM based multi-label learning with missing labels for image annotation

(2018) Yang Liu et al.   PATTERN RECOGNITION

Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

(2018) Renchu Guan et al.   Scientific Reports

Joint multi-label classification and label correlations with missing labels and feature selection

(2018) Zhi-Fen He et al.   KNOWLEDGE-BASED SYSTEMS

Multi-label learning of non-equilibrium labels completion with mean shift

(2018) Cheng Yusheng et al.   NEUROCOMPUTING

Selecting label-dependent features for multi-label classification

(2017) Lishan Qiao et al.   NEUROCOMPUTING

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

(2016) Jun Huang et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Multi-label learning with label-specific feature reduction

(2016) Suping Xu et al.   KNOWLEDGE-BASED SYSTEMS

A Tutorial on Multilabel Learning

(2015) Eva Gibaja et al.   ACM COMPUTING SURVEYS

Lift: Multi-Label Learning with Label-Specific Features

(2015) Min-Ling Zhang et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Trends in extreme learning machines: A review

(2015) Gao Huang et al.   NEURAL NETWORKS

An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels

(2014) Guang-Bin Huang   Cognitive Computation

LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification

(2014) Francisco Charte et al.   IEEE Transactions on Neural Networks and Learning Systems

A Review on Multi-Label Learning Algorithms

(2013) Min-Ling Zhang et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Classifier chains for multi-label classification

(2011) Jesse Read et al.   MACHINE LEARNING

A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

(2009) Amir Beck et al.   SIAM Journal on Imaging Sciences

Multilabel classification via calibrated label ranking

(2008) Johannes Fürnkranz et al.   MACHINE LEARNING