Yong et al., 2016 - Google Patents
Feature selection of unreliable data using an improved multi-objective PSO algorithmYong et al., 2016
- Document ID
- 15209193110890902953
- Author
- Yong Z
- Dun-wei G
- Wan-qiu Z
- Publication year
- Publication venue
- Neurocomputing
External Links
Snippet
Due to the influence of environment, data obtained in real world are not completely reliable sometimes. This paper focuses on tackling the feature selection problem with unreliable data. First, the problem is formulated as an multi-objective optimization one with two …
- 238000004422 calculation algorithm 0 title abstract description 62
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of features
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yong et al. | Feature selection of unreliable data using an improved multi-objective PSO algorithm | |
Xue et al. | A multi-objective particle swarm optimisation for filter-based feature selection in classification problems | |
Putatunda et al. | A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost | |
Raghu et al. | Evaluation of causal structure learning methods on mixed data types | |
Zhou et al. | Many-objective optimization of feature selection based on two-level particle cooperation | |
Zhao et al. | A cost sensitive decision tree algorithm based on weighted class distribution with batch deleting attribute mechanism | |
Pashaei et al. | Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data | |
Das et al. | Feature weighting and selection with a Pareto-optimal trade-off between relevancy and redundancy | |
Butler-Yeoman et al. | Particle swarm optimisation for feature selection: A hybrid filter-wrapper approach | |
Filatovas et al. | A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search | |
Wang et al. | A novel bacterial algorithm with randomness control for feature selection in classification | |
Xue et al. | A particle swarm optimisation based multi-objective filter approach to feature selection for classification | |
Rahman et al. | An n-state switching PSO algorithm for scalable optimization | |
Lin et al. | Evolutionary Multitasking for Multiobjective Feature Selection in Classification | |
Hamedmoghadam et al. | An opinion formation based binary optimization approach for feature selection | |
Xavier-Júnior et al. | A novel evolutionary algorithm for automated machine learning focusing on classifier ensembles | |
Zhou et al. | Imbalanced multifault diagnosis via improved localized feature selection | |
Khrissi et al. | A feature selection approach based on archimedes’ optimization algorithm for optimal data classification | |
Pasti et al. | Bio-inspired and gradient-based algorithms to train MLPs: The influence of diversity | |
Nikolikj et al. | Identifying minimal set of exploratory landscape analysis features for reliable algorithm performance prediction | |
Xue et al. | An archive based particle swarm optimisation for feature selection in classification | |
Le et al. | A hybrid surrogate model for evolutionary undersampling in imbalanced classification | |
Zafar et al. | An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II. | |
Cheng et al. | Maximizing receiver operating characteristics convex hull via dynamic reference point-based multi-objective evolutionary algorithm | |
Pashaei et al. | Random forest in splice site prediction of human genome |