Combining Pattern Classifiers: Methods and Algorithms

By Ludmila I. Kuncheva. 2004

2,417 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/191944930ce7db740d81908c4dc03614035cc5a2

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In Artificial Intelligence

Citations in the corpus

Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, methodology): “It has been shown that classifier Ensembles have great potential to outperform traditional single classifier systems especially on challenging recognition tasks [Kuncheva 2004].”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “The ensemble classiﬁer used for the fusion was a voting system that combines the a posteriori probabilities of the diﬀerent classiﬁers using the ‘Minimum Probability’ combination rule (Kuncheva, 2004; Kittler et al., 1998).”
Bayesian Hyperparameter Optimization for Ensemble Learning (2016, background): “For example, a diversity inducing metric such as pairwise disagreement (Kuncheva, 2004) could be used, but this would lead to degenerate pools of classifiers, as diversity is easily increased by trivial and degenerate classifiers (voting all for one class or the other).”
Mixture of experts: a literature survey (2012, positive): “In addition to categorising ME methods in these classes, as mentioned above, problem space partitioning in ME is based on dynamic switching between the selection and fusion or in other words, on the balance between hard splits of problem versus soft splits (Kuncheva 2004).”
Financial credit risk assessment: a recent review (2015, background): “Many researchers have provided the supporting evidences that MCS methodology can produce better performance than individual credit risk assessment models (Deligianni and Kotsiantis 2012; Kuncheva 2004; Rokach 2010; Verikas et al. 2010; Zhou 2012).”
An experimental comparison of performance measures for classification (2009, background): “In Multi-Classifier Systems (Kuncheva, 2004) many works have been devoted to study the resulting accuracy of an ensemble of combined classifiers given the original accuracies and some other conditions (Melnik et al., 2004; Narasimhamurthy, 2005; Kuncheva et al., 2003) or how to combine them in order”
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information (2014, methodology): “MLA architecture and the statistical distributions of observed data guides the training of classification models, which is usually carried out by minimizing a loss (error) function (Kuncheva, 2004; Marsland, 2009).”
Rotation Forest: A New Classifier Ensemble Method (2006, background): “Many studies have been published, both theoretical and empirical, which demonstrate the advantages of the combination paradigm over the individual classifier models [21].”