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Keyword

classifier diversity

Description: Classifier diversity describes differences in the predictions or errors of models within an ensemble, a property studied in ensemble learning.
OpenAlex ID: keywords/classifier-diversity
A survey of multiple classifier systems as hybrid systems
2013 · Michał Woźniak, Manuel Graña, et al. · Information Fusion · 913
A Survey on Ensemble Learning for Data Stream Classification
2017 · Heitor Murilo Gomes, Jean Paul Barddal, et al. · ACM Computing Surveys · 579
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
2013 · Mikel Galar, Alberto Fernández, et al. · Pattern Recognition · 419
Leveraging Bagging for Evolving Data Streams
2010 · Albert Bifet, Geoff Holmes, et al. · Lecture notes in computer science · 405
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Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates
2017 · Jie Sun, Jie Lang, et al. · Information Sciences · 385
Diversity in search strategies for ensemble feature selection
2004 · Alexey Tsymbal, Mykola Pechenizkiy, et al. · Information Fusion · 321
A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification
2017 · Aytuğ Onan, Serdar Korukoğlu, et al. · Information Processing & Management · 305
Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography
2009 · Lior Rokach · Computational Statistics & Data Analysis · 253
LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy
2013 · Chen Lin, Wenqiang Chen, et al. · Neurocomputing · 228
Random Subspace Ensembles for fMRI Classification
2010 · L.I. Kuncheva, J.J. Rodriguez, et al. · IEEE Transactions on Medical Imaging · 228
Hyperspectral Remote Sensing Image Classification Based on Rotation Forest
2013 · Junshi Xia, Peijun Du, et al. · IEEE Geoscience and Remote Sensing Letters · 215
Advance and Prospects of AdaBoost Algorithm
2014 · Ying CAO, Qi-Guang MIAO, et al. · Acta Automatica Sinica · 204
An approach to the automatic design of multiple classifier systems
2001 · Giorgio Giacinto, Fabio Roli · Pattern Recognition Letters · 197
Ensemble-based discriminant learning with boosting for face recognition
2006 · J. Lu, K.N. Plataniotis, et al. · IEEE Transactions on Neural Networks · 192
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Kappa Updated Ensemble for drifting data stream mining
2019 · Alberto Cano, Bartosz Krawczyk · Machine Learning · 185
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Accuracy/Diversity and Ensemble MLP Classifier Design
2006 · T. Windeatt · IEEE Transactions on Neural Networks · 168
Diversity in multiple classifier systems
2004 · Ludmila I. Kuncheva · Information Fusion · 165
Ensemble feature selection with the simple Bayesian classification
2003 · Alexey Tsymbal, Seppo Puuronen, et al. · Information Fusion · 161
A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine
2011 · Gang Wang, Jian Ma · Expert Systems with Applications · 160
Financial distress prediction using support vector machines: Ensemble vs. individual
2012 · Jie Sun, Hui Li · Applied Soft Computing · 153
Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring
2013 · Joaquín Abellán, Carlos J. Mantas · Expert Systems with Applications · 150
Combining bagging, boosting, rotation forest and random subspace methods
2010 · Sotiris Kotsiantis · Artificial Intelligence Review · 147
Diversity Regularized Ensemble Pruning
2012 · Nan Li, Yang Yu, et al. · Lecture notes in computer science · 146
Ten measures of diversity in classifier ensembles: limits for two classifiers
2001 · L.I. Kuncheva · 146
Cascaded Random Forest for Hyperspectral Image Classification
2018 · Youqiang Zhang, Guo Cao, et al. · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 145
Works count: 1,137
Citation count: 20,150
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