Related references
Note: Only part of the references are listed.Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection
Azam Asilian Bidgoli et al.
INFORMATION SCIENCES (2021)
Feature selection with missing labels based on label compression and local feature correlation
Lin Jiang et al.
NEUROCOMPUTING (2020)
Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Mohammad Tubishat et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection
An-Da Li et al.
INFORMATION SCIENCES (2020)
MLNE: Multi-Label Network Embedding
Min Shi et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)
Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy
Dexuan Zou et al.
APPLIED ENERGY (2019)
Majority voting for discrete population-based optimization algorithms
Sedigheh Mahdavi et al.
SOFT COMPUTING (2019)
Mutual information based multi-label feature selection via constrained convex optimization
Zhenqiang Sun et al.
NEUROCOMPUTING (2019)
A label-specific multi-label feature selection algorithm based on the Pareto dominance concept
Shima Kashef et al.
PATTERN RECOGNITION (2019)
An evolutionary gravitational search-based feature selection
Mohammad Taradeh et al.
INFORMATION SCIENCES (2019)
Speeding up k-Nearest Neighbors classifier for large-scale multi-label learning on GPUs
Przemyslaw Skryjomski et al.
NEUROCOMPUTING (2019)
An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch
Xin Shen et al.
ENERGY (2019)
Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems
Lin Sun et al.
IEEE ACCESS (2019)
Categorizing feature selection methods for multi-label classification
Rafael B. Pereira et al.
ARTIFICIAL INTELLIGENCE REVIEW (2018)
Multi-label feature selection via feature manifold learning and sparsity regularization
Zhiling Cai et al.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2018)
Effective Evolutionary Multilabel Feature Selection under a Budget Constraint
Jaesung Lee et al.
COMPLEXITY (2018)
Solving the dynamic economic dispatch by a memory-based global differential evolution and a repair technique of constraint handling
Dexuan Zou et al.
ENERGY (2018)
Multi-label feature selection with missing labels
Pengfei Zhu et al.
PATTERN RECOGNITION (2018)
Opposition based learning: A literature review
Sedigheh Mandavi et al.
SWARM AND EVOLUTIONARY COMPUTATION (2018)
Granular multi-label feature selection based on mutual information
Feng Li et al.
PATTERN RECOGNITION (2017)
Diversity Assessment in Many-Objective Optimization
Handing Wang et al.
IEEE TRANSACTIONS ON CYBERNETICS (2017)
A PSO-based multi-objective multilabel feature selection method in classification
Yong Zhang et al.
SCIENTIFIC REPORTS (2017)
Multi-label feature selection based on neighborhood mutual information
Yaojin Lin et al.
APPLIED SOFT COMPUTING (2016)
A Survey on Evolutionary Computation Approaches to Feature Selection
Bing Xue et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2016)
A systematic review of multi-label feature selection and a new method based on label construction
Newton Spolaor et al.
NEUROCOMPUTING (2016)
Opposition versus randomness in binary spaces
Z. Seif et al.
APPLIED SOFT COMPUTING (2015)
Mutual Information-based multi-label feature selection using interaction information
Jaesung Lee et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context
Oscar Reyes et al.
NEUROCOMPUTING (2015)
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2014)
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach
Bing Xue et al.
IEEE TRANSACTIONS ON CYBERNETICS (2013)
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Newton Spolaor et al.
ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE (2013)
ReliefF for Multi-label Feature Selection
Newton Spolaor et al.
2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS) (2013)
Irrelevant attributes and imbalanced classes in multi-label text-categorization domains
Sareewan Dendamrongvit et al.
INTELLIGENT DATA ANALYSIS (2011)
The weighted sum method for multi-objective optimization: new insights
R. Timothy Marler et al.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2010)
Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data
Chia-Ming Wang et al.
EXPERT SYSTEMS WITH APPLICATIONS (2009)
Opposition-based differential evolution
Shahryar Rahnamayan et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2008)
ML-KNN: A lazy learning approach to multi-label leaming
Min-Ling Zhang et al.
PATTERN RECOGNITION (2007)
A faster algorithm for calculating hypervolume
L While et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2006)
Learning multi-label scene classification
MR Boutell et al.
PATTERN RECOGNITION (2004)
A fast and elitist multiobjective genetic algorithm: NSGA-II
K Deb et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2002)