Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

By Ludmila I. Kuncheva, Christopher J. Whitaker. 2003

2,640 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

The Right Tool for the Job: Matching Model and Instance Complexities (2020, background): “com/tzhj3o8 Our approach should not be confused with model ensembles (Kuncheva and Whitaker, 2003), where the prediction of multiple models is combined, on every instance, in order to improve accuracy, at the expense of slower inference time.”
Striving for Simplicity in Off-policy Deep Reinforcement Learning (2019, background): “[33] Ludmila I Kuncheva and Christopher J Whitaker.”
Robust fine-tuning of zero-shot models (2021, background): “In certain cases, ensemble accuracy is correlated with diversity among the constituents (Kuncheva & Whitaker, 2003).”
An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning (2019, methodology): “It is a strategy [26, 85] to use multiple algorithms to improve machine learning performance, and which is proved to be effective to reduce the problems related to overfitting [35, 68].”
Practical Ensemble Classification Error Bounds for Different Operating Points (2013, background): “In either case, the tradeoff between the probabilities of false alarm and missed detection is important to take into account.”
Computer‐aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI (2018, methodology): “This is different from the ensemble learning, which builds several models to obtain better predict performance.(21) Enhanced prediction can save the time of building models and improve the predictive accuracy.”
Learning towards Minimum Hyperspherical Energy (2018, background): “Diversity regularization is shown useful in sparse coding [32, 35], ensemble learning [26, 24], selfpaced learning [21], metric learning [58], etc.”
Distilling Knowledge from Ensembles of Neural Networks for Speech Recognition (2016, background): “[15, 16]), models that tend to make errors on different inputs can compensate for one another in combination and make fewer errors than any model individually.”
A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, methodology): “Most of decision fusion techniques discussed in this paper are taken from Kuncheva’s work [2,11] and classifier ranking has been obtained from Ho et al. [3].”
Random forests: from early developments to recent advancements (2014, methodology): “Ensemble learning is a machine learning paradigm where multiple models are used to solve the same problem (Kuncheva & Whitaker, 2003; Polikar, 2006; Rokach, 2010).”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, background): “Kuncheva and Whitaker (2003) analyse ten different diversity measures for classifiers.”
Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, methodology): “Using ensemble techniques to improve classification is based on a sound theory [16; 70; 10; 44; 62].”