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Feature Selection for Ensembles Using the Multi-Objective Optimization Approach

https://doi.org/10.1007/11399346_3
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What this work says

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

inferredcites →https://doi.org/10.1007/978-1-4615-4369-5openalex-snapshot
Efficient and Accurate Parallel Genetic Algorithms
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An Introduction to the Bootstrap
inferredcites →https://doi.org/10.1007/978-3-540-44871-6_130openalex-snapshot
That Elusive Diversity in Classifier Ensembles
inferredcites →https://doi.org/10.1007/bf00117832openalex-snapshot
Stacked Regressions
inferredcites →https://doi.org/10.1007/s10032-003-0114-zopenalex-snapshot
Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach
inferredcites →https://doi.org/10.1007/s10044-002-0181-7openalex-snapshot
Input decimated ensembles
inferredcites →https://doi.org/10.1007/s100440200034openalex-snapshot
A Feature-Based Serial Approach to Classifier Combination
inferredcites →https://doi.org/10.1016/b978-1-55860-335-6.50023-4openalex-snapshot
Irrelevant Features and the Subset Selection Problem
inferredcites →https://doi.org/10.1016/s0031-3203(00)00059-5openalex-snapshot
Reject option with multiple thresholds
inferredcites →https://doi.org/10.1016/s0031-3203(99)00041-2openalex-snapshot
Comparison of algorithms that select features for pattern classifiers
inferredcites →https://doi.org/10.1016/s0031-3203(99)00223-xopenalex-snapshot
Decision templates for multiple classifier fusion: an experimental comparison
inferredcites →https://doi.org/10.1016/s0262-8856(01)00045-2openalex-snapshot
Design of effective neural network ensembles for image classification purposes
inferredcites →https://doi.org/10.1016/s0893-6080(96)00098-6openalex-snapshot
Optimal Linear Combinations of Neural Networks
inferredcites →https://doi.org/10.1016/s1566-2535(03)00004-6openalex-snapshot
Ensemble feature selection with the simple Bayesian classification
inferredcites →https://doi.org/10.1023/a:1022859003006openalex-snapshot
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
inferredcites →https://doi.org/10.1049/ic:20010105openalex-snapshot
Ten measures of diversity in classifier ensembles: limits for two classifiers
inferredcites →https://doi.org/10.1080/095400996116839openalex-snapshot
Error Correlation and Error Reduction in Ensemble Classifiers
inferredcites →https://doi.org/10.1109/34.667881openalex-snapshot
On combining classifiers
inferredcites →https://doi.org/10.1109/34.709601openalex-snapshot
The random subspace method for constructing decision forests
inferredcites →https://doi.org/10.1109/4235.887233openalex-snapshot
Designing classifier fusion systems by genetic algorithms
inferredcites →https://doi.org/10.1109/icdar.2003.1227746openalex-snapshot
Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition
inferredcites →https://doi.org/10.1109/icsmc.1999.814176openalex-snapshot
A parallel genetic algorithm with distributed environment scheme
inferredcites →https://doi.org/10.1109/icsmc.1999.825221openalex-snapshot
A two-phase feature selection method using both filter and wrapper
inferredcites →https://doi.org/10.1109/iwfhr.2002.1030906openalex-snapshot
Creation of classifier ensembles for handwritten word recognition using feature selection algorithms
inferredcites →https://doi.org/10.1109/sibgra.2002.1167145openalex-snapshot
Evaluating NN and HMM classifiers for handwritten word recognition
inferredcites →https://doi.org/10.1109/tpami.1979.4766909openalex-snapshot
A Cluster Separation Measure
inferredcites →https://doi.org/10.1109/tpami.2002.1046154openalex-snapshot
Automatic recognition of handwritten numerical strings: a recognition and verification strategy
inferredcites →https://doi.org/10.1142/s021800140300271xopenalex-snapshot
A Methodology for Feature Selection Using Multiobjective Genetic Algorithms for Handwritten Digit String Recognition
inferredcites →https://doi.org/10.1145/347090.347169openalex-snapshot
Feature selection in unsupervised learning via evolutionary search
inferredcites →https://doi.org/10.1162/evco.1994.2.3.221openalex-snapshot
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
inferredcites →https://doi.org/10.1162/neco.1996.8.4.869openalex-snapshot
Engineering Multiversion Neural-Net Systems
inferredcites →https://doi.org/10.1201/9780429246593openalex-snapshot
An Introduction to the Bootstrap
inferredcites →https://doi.org/10.1887/0750306653openalex-snapshot
Evolutionary Computation 2

What points at this work

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

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Multi‐objective ensemble generation
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Rough Set Based Decision Support—Models Easy to Interpret
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A Neuroevolutionary Approach to Feature Selection Using Multiobjective Evolutionary Algorithms
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Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: A Survey and a Case Study (Application to Music Categorisation)
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Feature Selection Using Tabu Search with Learning Memory: Learning Tabu Search
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Attribute Selection in a Dispersed Decision-Making System
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Feature Selection Using Single/Multi-Objective Memetic Frameworks
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A New Approach to Improving ICA-Based Models for the Classification of Microarray Data
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Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach
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Feature Selection Using Multi-Objective Evolutionary Algorithms: Application to Cardiac SPECT Diagnosis
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A Comparative Study of Microarray Data Classification Methods Based on Ensemble Biological Relevant Gene Sets
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Multi-Objective Evolutionary Algorithms for Feature Selection: Application in Bankruptcy Prediction
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The Design of Evolutionary Multiple Classifier System for the Classification of Microarray Data
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A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation
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Introduction
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Probabilistic ensemble Fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
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Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method
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A Multi-objective Optimization Approach for the Synthesis of Granular Computing-Based Classification Systems in the Graph Domain
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A novel ensemble of classifiers that use biological relevant gene sets for microarray classification
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Ensemble learning-based stability improvement method for feature selection towards performance prediction
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Ensemble component selection for improving ICA based microarray data prediction models
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Comparison of fusion methods from the abstract level and the rank level in a dispersed decision-making system
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Genetic algorithm with different feature selection method for intrusion detection
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Multi-objective genetic algorithm approach to feature subset optimization
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Feature selection based on the rough set theory and dispersed system with dynamically generated disjoint clusters
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Identification of Full and Partial Class Relevant Genes
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Feature Selection for Bankruptcy Prediction
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Extraire et exploiter la connaissance pour mieux optimiser
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