On the public citation graph

An analysis of diversity measures

https://doi.org/10.1007/s10994-006-9449-2
0 signed · 413 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1002/0471445428openalex-snapshot
Statistical Methods for Rates and Proportions
inferredcites →https://doi.org/10.1006/inco.1995.1136openalex-snapshot
Boosting a Weak Learning Algorithm by Majority
inferredcites →https://doi.org/10.1007/3-540-45164-1_41openalex-snapshot
On the Boosting Pruning Problem
inferredcites →https://doi.org/10.1007/3-540-49097-3_1openalex-snapshot
Theoretical Views of Boosting
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-3-540-44871-6_130openalex-snapshot
That Elusive Diversity in Classifier Ensembles
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/s0925-2312(02)00603-3openalex-snapshot
Boosting the HONG network
inferredcites →https://doi.org/10.1016/s0950-5849(97)00023-2openalex-snapshot
Software diversity: practical statistics for its measurement and exploitation
inferredcites →https://doi.org/10.1023/a:1007515423169openalex-snapshot
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
inferredcites →https://doi.org/10.1023/a:1007607513941openalex-snapshot
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
inferredcites →https://doi.org/10.1023/a:1007618119488openalex-snapshot
Soft Margins for AdaBoost
inferredcites →https://doi.org/10.1023/a:1007697429651openalex-snapshot
Improved Generalization Through Explicit Optimization of Margins
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1023/a:1018054314350openalex-snapshot
Bagging Predictors
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/10.1109/34.58871openalex-snapshot
Neural network ensembles
inferredcites →https://doi.org/10.1109/34.709601openalex-snapshot
The random subspace method for constructing decision forests
inferredcites →https://doi.org/10.1109/4235.887237openalex-snapshot
Evolutionary ensembles with negative correlation learning
inferredcites →https://doi.org/10.1109/64.539013openalex-snapshot
Data mining and knowledge discovery: making sense out of data
inferredcites →https://doi.org/10.1109/72.737507openalex-snapshot
Hierarchical overlapped SOM's for pattern classification
inferredcites →https://doi.org/10.1142/5089openalex-snapshot
Least Squares Support Vector Machines
inferredcites →https://doi.org/10.1214/aos/1024691352openalex-snapshot
Boosting the margin: a new explanation for the effectiveness of voting methods

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4103194openalex-extracted
Application of Ensembles for Stock Index Price Prediction
inferred← citeshttps://doi.org/10.2139/ssrn.4495110openalex-extracted
Hierarchical Evolutionary Ensemble of Multiple Classifiers
inferred← citeshttps://doi.org/10.2139/ssrn.4601010openalex-extracted
Hierarchical Evolutionary Ensemble of Multiple Classifiers
inferred← citeshttps://doi.org/10.1002/9781118914564.refsopenalex-snapshot
References
inferred← citeshttps://doi.org/10.1002/we.411openalex-snapshot
Short‐term wind speed prediction in wind farms based on banks of support vector machines
inferred← citeshttps://doi.org/10.1002/widm.1158openalex-snapshot
Multi‐objective ensemble generation
inferred← citeshttps://doi.org/10.1007/978-1-4471-2760-4_6openalex-snapshot
Rough Set Based Decision Support—Models Easy to Interpret
inferred← citeshttps://doi.org/10.1007/978-1-4471-5571-3_20openalex-snapshot
Combining Multiple Learners: Data Fusion and Emsemble Learning
inferred← citeshttps://doi.org/10.1007/978-1-4471-7452-3_25openalex-snapshot
Combining Multiple Learners: Data Fusion and Ensemble Learning
inferred← citeshttps://doi.org/10.1007/978-1-4939-1456-2_5openalex-snapshot
Ensemble Learning Approaches in Speech Recognition
inferred← citeshttps://doi.org/10.1007/978-3-030-03496-2_30openalex-snapshot
EMnGA: Entropy Measure and Genetic Algorithms Based Method for Heterogeneous Ensembles Selection
inferred← citeshttps://doi.org/10.1007/978-3-030-06222-4_19openalex-snapshot
Metaheuristics and Classifier Ensembles
inferred← citeshttps://doi.org/10.1007/978-3-030-12931-6_9openalex-snapshot
Tsallis Entropy for Image Thresholding
inferred← citeshttps://doi.org/10.1007/978-3-030-38006-9_3openalex-snapshot
Usage of Machine Learning in Software Testing
inferred← citeshttps://doi.org/10.1007/978-3-030-41404-7_11openalex-snapshot
Robust Pedestrian Detection: Faster Deployments with Fusion of Models
inferred← citeshttps://doi.org/10.1007/978-3-030-62362-3_29openalex-snapshot
Entropy Based Grey Wolf Optimizer
inferred← citeshttps://doi.org/10.1007/978-3-030-63007-2_28openalex-snapshot
A Method for Building Heterogeneous Ensembles of Regression Models Based on a Genetic Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-030-72699-7_40openalex-snapshot
Optimising Diversity in Classifier Ensembles of Classification Trees
inferred← citeshttps://doi.org/10.1007/978-3-030-83508-8_46openalex-snapshot
Diverse Partitions of Colored Points
inferred← citeshttps://doi.org/10.1007/978-3-319-10840-7_11openalex-snapshot
Diversified Random Forests Using Random Subspaces
inferred← citeshttps://doi.org/10.1007/978-3-319-12436-0_33openalex-snapshot
A Dynamic Generation Approach for Ensemble of Extreme Learning Machines
inferred← citeshttps://doi.org/10.1007/978-3-319-20248-8_18openalex-snapshot
An Empirical Investigation on the Use of Diversity for Creation of Classifier Ensembles
inferred← citeshttps://doi.org/10.1007/978-3-319-20248-8_3openalex-snapshot
Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble
inferred← citeshttps://doi.org/10.1007/978-3-319-24865-3_5openalex-snapshot
An Online Learning-Based Adaptive Biometric System
inferred← citeshttps://doi.org/10.1007/978-3-319-25032-8_2openalex-snapshot
A Replicator Dynamics Approach to Collective Feature Engineering in Random Forests
inferred← citeshttps://doi.org/10.1007/978-3-319-25032-8_4openalex-snapshot
CLUB-DRF: A Clustering Approach to Extreme Pruning of Random Forests
inferred← citeshttps://doi.org/10.1007/978-3-319-44188-7_20openalex-snapshot
An Outlier Ranking Tree Selection Approach to Extreme Pruning of Random Forests
inferred← citeshttps://doi.org/10.1007/978-3-319-44944-9_7openalex-snapshot
Increasing Diversity in Random Forests Using Naive Bayes
inferred← citeshttps://doi.org/10.1007/978-3-319-51109-2_2openalex-snapshot
Multilevel Segmentation in Digital Images
inferred← citeshttps://doi.org/10.1007/978-3-319-69179-4_21openalex-snapshot
Effects of Dynamic Subspacing in Random Forest
inferred← citeshttps://doi.org/10.1007/978-3-319-92013-9_22openalex-snapshot
Auto-CES: An Automatic Pruning Method Through Clustering Ensemble Selection
inferred← citeshttps://doi.org/10.1007/978-3-540-69828-9_18openalex-snapshot
Combining One-Class Classification Models Based on Diverse Biological Data for Prediction of Protein-Protein Interactions
inferred← citeshttps://doi.org/10.1007/978-3-540-79721-0_17openalex-snapshot
Stability Analysis on Rough Set Based Feature Evaluation
inferred← citeshttps://doi.org/10.1007/978-3-540-87656-4_93openalex-snapshot
Effects of Diversity Measures on the Design of Ensemble Classifiers by Multiobjective Genetic Fuzzy Rule Selection with a Multi-classifier Coding Scheme
inferred← citeshttps://doi.org/10.1007/978-3-642-02326-2_24openalex-snapshot
True Path Rule Hierarchical Ensembles
inferred← citeshttps://doi.org/10.1007/978-3-642-02326-2_44openalex-snapshot
Diversity-Based Classifier Selection for Adaptive Object Tracking
inferred← citeshttps://doi.org/10.1007/978-3-642-03999-7_1openalex-snapshot
An Ensemble Pruning Primer
inferred← citeshttps://doi.org/10.1007/978-3-642-05224-8_16openalex-snapshot
Averaged Naive Bayes Trees: A New Extension of AODE
inferred← citeshttps://doi.org/10.1007/978-3-642-14055-6_25openalex-snapshot
Measuring Impact of Diversity of Classifiers on the Accuracy of Evidential Ensemble Classifiers
inferred← citeshttps://doi.org/10.1007/978-3-642-17537-4_67openalex-snapshot
Non–uniform Layered Clustering for Ensemble Classifier Generation and Optimality
inferred← citeshttps://doi.org/10.1007/978-3-642-21111-9_58openalex-snapshot
The Design of Evolutionary Multiple Classifier System for the Classification of Microarray Data
inferred← citeshttps://doi.org/10.1007/978-3-642-21557-5_27openalex-snapshot
Analyzing the Relationship between Diversity and Evidential Fusion Accuracy
inferred← citeshttps://doi.org/10.1007/978-3-642-21587-2_8openalex-snapshot
Ensemble of Classifiers Based on Hard Instances
inferred← citeshttps://doi.org/10.1007/978-3-642-24965-5_32openalex-snapshot
An Evolutionary Algorithm Based Optimization of Neural Ensemble Classifiers
inferred← citeshttps://doi.org/10.1007/978-3-642-27222-6_14openalex-snapshot
Ensemble Learning
inferred← citeshttps://doi.org/10.1007/978-3-642-30867-3_5openalex-snapshot
An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity
inferred← citeshttps://doi.org/10.1007/978-3-642-33460-3_27openalex-snapshot
Diversity Regularized Ensemble Pruning
inferred← citeshttps://doi.org/10.1007/978-3-642-34481-7_13openalex-snapshot
Classifier Ensemble Using a Heuristic Learning with Sparsity and Diversity
inferred← citeshttps://doi.org/10.1007/978-3-642-35286-7_50openalex-snapshot
A New Diversity Measure for Classifier Fusion
inferred← citeshttps://doi.org/10.1007/978-3-642-38067-9_4openalex-snapshot
Diversity in Classifier Ensembles: Fertile Concept or Dead End?
inferred← citeshttps://doi.org/10.1007/978-3-642-39742-4_2openalex-snapshot
Some Recent Work on Multi-objective Approaches to Search-Based Software Engineering
inferred← citeshttps://doi.org/10.1007/978-3-642-41278-3_44openalex-snapshot
Gradient Boosting-Based Negative Correlation Learning
inferred← citeshttps://doi.org/10.1007/978-981-10-3002-4_50openalex-snapshot
Combination of Multiple Classifier Using Feature Space Partitioning
inferred← citeshttps://doi.org/10.1007/978-981-15-1967-3_8openalex-snapshot
Ensemble Learning
inferred← citeshttps://doi.org/10.1007/978-981-96-5589-2_17openalex-snapshot
Active Learning Using Divergence-Based Sampling in Ensemble Framework of Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1007/s00500-015-1927-7openalex-snapshot
Integration of classifier diversity measures for feature selection-based classifier ensemble reduction
inferred← citeshttps://doi.org/10.1007/s00500-021-05671-yopenalex-snapshot
A distributed ensemble of relevance vector machines for large-scale data sets on Spark
inferred← citeshttps://doi.org/10.1007/s00521-019-04359-7openalex-snapshot
A dynamic ensemble learning algorithm for neural networks
inferred← citeshttps://doi.org/10.1007/s00607-019-00785-6openalex-snapshot
Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach
inferred← citeshttps://doi.org/10.1007/s10115-026-02696-3openalex-snapshot
A transformer-based stacked ensemble framework for software defect prediction
inferred← citeshttps://doi.org/10.1007/s10462-020-09922-6openalex-snapshot
Ensemble learning based on random super-reduct and resampling
inferred← citeshttps://doi.org/10.1007/s10479-016-2328-8openalex-snapshot
Distant diversity in dynamic class prediction
inferred← citeshttps://doi.org/10.1007/s10479-022-04599-2openalex-snapshot
Data-driven decision model based on local two-stage weighted ensemble learning
inferred← citeshttps://doi.org/10.1007/s10489-009-0199-2openalex-snapshot
Ensembles of ARTMAP-based neural networks: an experimental study
inferred← citeshttps://doi.org/10.1007/s10489-013-0510-0openalex-snapshot
Ensemble selection by GRASP
inferred← citeshttps://doi.org/10.1007/s10489-017-0922-3openalex-snapshot
Creating diversity in ensembles using synthetic neighborhoods of training samples
inferred← citeshttps://doi.org/10.1007/s10489-017-1027-8openalex-snapshot
Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast
inferred← citeshttps://doi.org/10.1007/s10489-017-1106-xopenalex-snapshot
Maximum relevancy maximum complementary based ordered aggregation for ensemble pruning
inferred← citeshttps://doi.org/10.1007/s10618-017-0523-0openalex-snapshot
Selective harvesting over networks
inferred← citeshttps://doi.org/10.1007/s10618-019-00638-yopenalex-snapshot
A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates
inferred← citeshttps://doi.org/10.1007/s10726-020-09667-1openalex-snapshot
Coalitions’ Weights in a Dispersed System with Pawlak Conflict Model
inferred← citeshttps://doi.org/10.1007/s10796-018-9889-9openalex-snapshot
Is Self-Citation Biased? An Investigation via the Lens of Citation Polarity, Density, and Location
inferred← citeshttps://doi.org/10.1007/s10796-023-10395-5openalex-snapshot
Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels
inferred← citeshttps://doi.org/10.1007/s10844-017-0446-7openalex-snapshot
Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data
inferred← citeshttps://doi.org/10.1007/s10994-010-5172-0openalex-snapshot
An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
inferred← citeshttps://doi.org/10.1007/s10994-023-06305-0openalex-snapshot
Diverse and consistent multi-view networks for semi-supervised regression
inferred← citeshttps://doi.org/10.1007/s10994-023-06406-wopenalex-snapshot
A geometric framework for multiclass ensemble classifiers
inferred← citeshttps://doi.org/10.1007/s10994-023-06429-3openalex-snapshot
Margin distribution and structural diversity guided ensemble pruning
inferred← citeshttps://doi.org/10.1007/s10994-024-06604-0openalex-snapshot
Deep negative correlation classification
inferred← citeshttps://doi.org/10.1007/s11047-007-9063-7openalex-snapshot
Negative correlation in incremental learning
inferred← citeshttps://doi.org/10.1007/s11047-020-09791-6openalex-snapshot
Ensemble learning based on fitness Euclidean-distance ratio differential evolution for classification
inferred← citeshttps://doi.org/10.1007/s11063-017-9703-6openalex-snapshot
Pruning the Ensemble of ANN Based on Decision Tree Induction
inferred← citeshttps://doi.org/10.1007/s11063-018-9924-3openalex-snapshot
LocalBoost: A Parallelizable Approach to Boosting Classifiers
inferred← citeshttps://doi.org/10.1007/s11063-020-10336-2openalex-snapshot
Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization
inferred← citeshttps://doi.org/10.1007/s11063-022-10807-8openalex-snapshot
An Updatable Classifier Diversity Measure Based on the ER Rule
inferred← citeshttps://doi.org/10.1007/s11227-020-03196-zopenalex-snapshot
MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
inferred← citeshttps://doi.org/10.1007/s11704-018-7151-8openalex-snapshot
Structural diversity for decision tree ensemble learning
inferred← citeshttps://doi.org/10.1007/s12065-020-00519-0openalex-snapshot
A novel approach to build accurate and diverse decision tree forest
inferred← citeshttps://doi.org/10.1007/s12559-024-10257-5openalex-snapshot
Two-layer Ensemble of Deep Learning Models for Medical Image Segmentation
inferred← citeshttps://doi.org/10.1007/s13042-012-0094-8openalex-snapshot
Performance of global–local hybrid ensemble versus boosting and bagging ensembles
inferred← citeshttps://doi.org/10.1007/s13042-014-0303-8openalex-snapshot
Towards an optimally pruned classifier ensemble
inferred← citeshttps://doi.org/10.1007/s13042-015-0333-xopenalex-snapshot
A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority
inferred← citeshttps://doi.org/10.1007/s13042-015-0366-1openalex-snapshot
An online ensembles approach for handling concept drift in data streams: diversified online ensembles detection
inferred← citeshttps://doi.org/10.1007/s13042-017-0732-2openalex-snapshot
A dynamic selection ensemble method for target recognition based on clustering and randomized reference classifier
inferred← citeshttps://doi.org/10.1007/s13748-014-0042-9openalex-snapshot
Accuracy–diversity based pruning of classifier ensembles
inferred← citeshttps://doi.org/10.1007/s42979-020-0119-4openalex-snapshot
Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems
inferred← citeshttps://doi.org/10.1007/s42979-022-01063-7openalex-snapshot
Optimising Diversity in Classifier Ensembles
inferred← citeshttps://doi.org/10.1016/b978-0-12-417295-1.00020-5openalex-snapshot
Ensembles of Learning Machines
inferred← citeshttps://doi.org/10.1016/b978-0-12-417295-1.09988-4openalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1016/b978-0-44-319077-3.00015-8openalex-snapshot
The power of voting
inferred← citeshttps://doi.org/10.1016/j.asoc.2014.10.017openalex-snapshot
Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines
inferred← citeshttps://doi.org/10.1016/j.asoc.2015.12.016openalex-snapshot
Global decision-making in multi-agent decision-making system with dynamically generated disjoint clusters
inferred← citeshttps://doi.org/10.1016/j.asoc.2017.10.004openalex-snapshot
DRED: An evolutionary diversity generation method for concept drift adaptation in online learning environments
inferred← citeshttps://doi.org/10.1016/j.asoc.2018.09.021openalex-snapshot
Combining heterogeneous classifiers via granular prototypes
inferred← citeshttps://doi.org/10.1016/j.asoc.2021.107689openalex-snapshot
Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109136openalex-snapshot
Dynamic environment prediction on unmanned mobile manipulator robot via ensemble convolutional randomization networks
inferred← citeshttps://doi.org/10.1016/j.asoc.2026.116016openalex-snapshot
DengueCare: A generative artificial intelligence and fuzzy reasoning approach to improving hospitalization predictions in dengue patients
inferred← citeshttps://doi.org/10.1016/j.bdr.2017.07.003openalex-snapshot
Random Forests for Big Data
inferred← citeshttps://doi.org/10.1016/j.ces.2018.05.037openalex-snapshot
The soft sensor of the molten steel temperature using the modified maximum entropy based pruned bootstrap feature subsets ensemble method
inferred← citeshttps://doi.org/10.1016/j.cie.2018.12.021openalex-snapshot
Improved discrete artificial fish swarm algorithm combined with margin distance minimization for ensemble pruning
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105675openalex-snapshot
Improving predictive performance on survival in dairy cattle using an ensemble learning approach
inferred← citeshttps://doi.org/10.1016/j.compind.2018.03.038openalex-snapshot
Using a classifier ensemble for proactive quality monitoring and control: The impact of the choice of classifiers types, selection criterion, and fusion process
inferred← citeshttps://doi.org/10.1016/j.compind.2018.04.002openalex-snapshot
A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis
inferred← citeshttps://doi.org/10.1016/j.dajour.2023.100218openalex-snapshot
An Artificial Intelligence model for smart post-stroke assessment using wearable sensors
inferred← citeshttps://doi.org/10.1016/j.datak.2023.102232openalex-snapshot
A novel ensemble framework driven by diversity and cooperativity for non-stationary data stream classification
inferred← citeshttps://doi.org/10.1016/j.ejor.2015.05.030openalex-snapshot
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
inferred← citeshttps://doi.org/10.1016/j.ejor.2016.02.008openalex-snapshot
The strength of coalition in a dispersed decision support system with negotiations
inferred← citeshttps://doi.org/10.1016/j.enconman.2024.118471openalex-snapshot
A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zones
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.05.006openalex-snapshot
New diversity measure for data stream classification ensembles
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.04.010openalex-snapshot
method: NOV@
inferred← citeshttps://doi.org/10.1016/j.eswa.2015.03.028openalex-snapshot
Improving segmentation velocity using an evolutionary method
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.08.002openalex-snapshot
Forest PA : Constructing a decision forest by penalizing attributes used in previous trees
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.119845openalex-snapshot
Classifier ensemble with evolutionary optimisation enforced random projections
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121269openalex-snapshot
A Genetic Algorithm-based sequential instance selection framework for ensemble learning
inferred← citeshttps://doi.org/10.1016/j.future.2024.06.051openalex-snapshot
Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers
inferred← citeshttps://doi.org/10.1016/j.ijar.2011.12.011openalex-snapshot
The impact of diversity on the accuracy of evidential classifier ensembles
inferred← citeshttps://doi.org/10.1016/j.inffus.2013.04.006openalex-snapshot
A survey of multiple classifier systems as hybrid systems
inferred← citeshttps://doi.org/10.1016/j.inffus.2013.11.003openalex-snapshot
Convex ensemble learning with sparsity and diversity
inferred← citeshttps://doi.org/10.1016/j.inffus.2014.05.006openalex-snapshot
Partially-supervised learning from facial trajectories for face recognition in video surveillance
inferred← citeshttps://doi.org/10.1016/j.inffus.2016.11.006openalex-snapshot
Fusion of multiple diverse predictors in stock market
inferred← citeshttps://doi.org/10.1016/j.infsof.2017.03.007openalex-snapshot
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
inferred← citeshttps://doi.org/10.1016/j.ins.2010.12.004openalex-snapshot
Ectropy of diversity measures for populations in Euclidean space
inferred← citeshttps://doi.org/10.1016/j.ins.2014.03.105openalex-snapshot
Population-based Algorithm Portfolios with automated constituent algorithms selection
inferred← citeshttps://doi.org/10.1016/j.ins.2014.07.032openalex-snapshot
A dispersed decision-making system – The use of negotiations during the dynamic generation of a system’s structure
inferred← citeshttps://doi.org/10.1016/j.ins.2019.03.067openalex-snapshot
A weighted multiple classifier framework based on random projection
inferred← citeshttps://doi.org/10.1016/j.is.2017.05.002openalex-snapshot
Dispersed decision-making system with fusion methods from the rank level and the measurement level – A comparative study
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2010.08.007openalex-snapshot
Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests
inferred← citeshttps://doi.org/10.1016/j.jngse.2015.02.012openalex-snapshot
Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization
inferred← citeshttps://doi.org/10.1016/j.jtbi.2007.10.007openalex-snapshot
A quantitative investigation of the chemical space surrounding amino acid alphabet formation
inferred← citeshttps://doi.org/10.1016/j.knosys.2013.01.002openalex-snapshot
Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm
inferred← citeshttps://doi.org/10.1016/j.knosys.2014.06.005openalex-snapshot
Exploiting diversity for optimizing margin distribution in ensemble learning
inferred← citeshttps://doi.org/10.1016/j.knosys.2016.07.016openalex-snapshot
Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106517openalex-snapshot
A hierarchical fusion framework to integrate homogeneous and heterogeneous classifiers for medical decision-making
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.108505openalex-snapshot
Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.110067openalex-snapshot
Uncertainty guided pruning of classification model tree
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.111103openalex-snapshot
Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.116652openalex-snapshot
Bounds on kappa-error plots for classification diversity analysis
inferred← citeshttps://doi.org/10.1016/j.neucom.2008.09.020openalex-snapshot
Combined projection and kernel basis functions for classification in evolutionary neural networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2008.09.022openalex-snapshot
Selective negative correlation learning approach to incremental learning
inferred← citeshttps://doi.org/10.1016/j.neucom.2012.04.007openalex-snapshot
Margin optimization based pruning for random forest
inferred← citeshttps://doi.org/10.1016/j.neucom.2013.07.054openalex-snapshot
A novel classifier ensemble method with sparsity and diversity
inferred← citeshttps://doi.org/10.1016/j.neucom.2014.02.030openalex-snapshot
A spectral clustering based ensemble pruning approach
inferred← citeshttps://doi.org/10.1016/j.neucom.2014.07.065openalex-snapshot
Confidence ratio affinity propagation in ensemble selection of neural network classifiers for distributed privacy-preserving data mining
inferred← citeshttps://doi.org/10.1016/j.neucom.2014.10.086openalex-snapshot
Relating ensemble diversity and performance: A study in class noise detection
inferred← citeshttps://doi.org/10.1016/j.neucom.2016.01.045openalex-snapshot
A novel Supervised Competitive Learning algorithm
inferred← citeshttps://doi.org/10.1016/j.neucom.2016.08.013openalex-snapshot
Meta-learning recommendation of default size of classifier pool for META-DES
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.05.029openalex-snapshot
A non-specialized ensemble classifier using multi-objective optimization
inferred← citeshttps://doi.org/10.1016/j.neucom.2022.09.009openalex-snapshot
Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126516openalex-snapshot
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble
inferred← citeshttps://doi.org/10.1016/j.neunet.2023.12.025openalex-snapshot
Meta-structure-based graph attention networks
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2017.07.009openalex-snapshot
Detection and prediction of segments containing extreme significant wave heights
inferred← citeshttps://doi.org/10.1016/j.patcog.2008.09.036openalex-snapshot
Threshold-optimized decision-level fusion and its application to biometrics
inferred← citeshttps://doi.org/10.1016/j.patcog.2010.04.013openalex-snapshot
Information theoretic combination of pattern classifiers
inferred← citeshttps://doi.org/10.1016/j.patcog.2010.07.021openalex-snapshot
Sparse ensembles using weighted combination methods based on linear programming
inferred← citeshttps://doi.org/10.1016/j.patcog.2011.12.016openalex-snapshot
Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition
inferred← citeshttps://doi.org/10.1016/j.patcog.2013.04.003openalex-snapshot
Selective multiple kernel learning for classification with ensemble strategy
inferred← citeshttps://doi.org/10.1016/j.patcog.2014.03.021openalex-snapshot
Exploration of classification confidence in ensemble learning
inferred← citeshttps://doi.org/10.1016/j.patcog.2015.07.010openalex-snapshot
Ensembles of random sphere cover classifiers
inferred← citeshttps://doi.org/10.1016/j.patcog.2016.03.017openalex-snapshot
Cost-effectiveness of classification ensembles
inferred← citeshttps://doi.org/10.1016/j.patcog.2019.107104openalex-snapshot
Ensemble Selection based on Classifier Prediction Confidence
inferred← citeshttps://doi.org/10.1016/j.patcog.2024.111155openalex-snapshot
Negatively correlated ensemble against transfer adversarial attacks
inferred← citeshttps://doi.org/10.1016/j.patrec.2016.01.029openalex-snapshot
Combining diversity measures for ensemble pruning
inferred← citeshttps://doi.org/10.1016/j.patter.2021.100415openalex-snapshot
Ranks underlie outcome of combining classifiers: Quantitative roles for and
inferred← citeshttps://doi.org/10.1016/j.patter.2024.100924openalex-snapshot
The DIRAC framework: Geometric structure underlies roles of and in combining classifiers
inferred← citeshttps://doi.org/10.1016/j.solener.2017.07.052openalex-snapshot
An ensemble prediction intervals approach for short-term PV power forecasting
inferred← citeshttps://doi.org/10.1016/j.tra.2020.04.005openalex-snapshot
Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data
inferred← citeshttps://doi.org/10.1016/j.ymeth.2015.04.006openalex-snapshot
Network-constrained forest for regularized classification of omics data
inferred← citeshttps://doi.org/10.1016/j.ymeth.2015.08.016openalex-snapshot
Predicting protein function and other biomedical characteristics with heterogeneous ensembles
inferred← citeshttps://doi.org/10.1017/s0140525x23002212openalex-snapshot
The future of experimental design: Integrative, but is the sample diverse enough?
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00408openalex-snapshot
Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence
inferred← citeshttps://doi.org/10.1021/acs.est.1c04076openalex-snapshot
Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations
inferred← citeshttps://doi.org/10.1038/s41467-024-51433-3openalex-snapshot
An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles
inferred← citeshttps://doi.org/10.1080/00207721.2017.1381283openalex-snapshot
A target recognition method for maritime surveillance radars based on hybrid ensemble selection
inferred← citeshttps://doi.org/10.1080/01691864.2014.909293openalex-snapshot
A semi-supervised learning system for service robots to recognise human actions
inferred← citeshttps://doi.org/10.1080/01969722.2012.688684openalex-snapshot
INTEGRATING GLOBAL AND LOCAL VOTING OF CLASSIFIERS
inferred← citeshttps://doi.org/10.1080/03081079.2017.1314276openalex-snapshot
Comparison of fusion methods from the abstract level and the rank level in a dispersed decision-making system
inferred← citeshttps://doi.org/10.1093/bib/bbac344openalex-snapshot
The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma
inferred← citeshttps://doi.org/10.1093/bioinformatics/btn644openalex-snapshot
A genetic programming-based approach to the classification of multiclass microarray datasets
inferred← citeshttps://doi.org/10.1093/comjnl/bxu050openalex-snapshot
A Selective Detector Ensemble for Concept Drift Detection
inferred← citeshttps://doi.org/10.1109/access.2019.2899389openalex-snapshot
An Oil Painters Recognition Method Based on Cluster Multiple Kernel Learning Algorithm
inferred← citeshttps://doi.org/10.1109/access.2019.2917620openalex-snapshot
Diversity in Machine Learning
inferred← citeshttps://doi.org/10.1109/access.2020.3047057openalex-snapshot
Multi-Layer Combinatorial Fusion Using Cognitive Diversity
inferred← citeshttps://doi.org/10.1109/access.2021.3060768openalex-snapshot
Simple Fusion of Object Detectors for Improved Performance and Faster Deployment
inferred← citeshttps://doi.org/10.1109/access.2021.3063254openalex-snapshot
DDES: A Distribution-Based Dynamic Ensemble Selection Framework
inferred← citeshttps://doi.org/10.1109/access.2023.3280048openalex-snapshot
Anomaly Detection Using Ensemble Classification and Evidence Theory
inferred← citeshttps://doi.org/10.1109/adconip.2017.7983849openalex-snapshot
Handling class imbalance and multiple inspection objectives in design of industrial inspection system
inferred← citeshttps://doi.org/10.1109/bigdata.2018.8622038openalex-snapshot
Clustering-Driven and Dynamically Diversified Ensemble for Drifting Data Streams
inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11402632openalex-snapshot
A Diversity-Optimized Deep Ensemble Approach for Accurate Plant Leaf Disease Detection
inferred← citeshttps://doi.org/10.1109/cai64502.2025.00189openalex-snapshot
Automl Framework for Ensembling: Split Validation Sets for Coefficient and Structure Selection
inferred← citeshttps://doi.org/10.1109/cec.2009.4983046openalex-snapshot
The diversity/accuracy dilemma: An empirical analysis in the context of heterogeneous ensembles
showing the first 200 of 389
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.