Managing Diversity in Regression Ensembles

By Gavin Brown, Jeremy L. Wyatt, Peter Tino. Journal of machine learning research, 2005

410 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9aee764d4c7d9d1361a3629be6a317c0e8c73d99

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Citations in the corpus

Diversity analysis on imbalanced data sets by using ensemble models (2009, background): “When discussing about diversity, we cannot ignore accuracy, because there is a trade-off between accuracy of each classifier and ensemble diversity [18], [17].”
A dynamic ensemble learning algorithm for neural networks (2019, background): “There are evidences that accurate and diverse NNs can produce a good ensemble that distribute errors over different regions of the input space [38, 39].”
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning (2020, background): “Ensemble works for random features / NTK. Traditional theorems (Alhamdoosh & Wang, 2014; Brown et al., 2005a; Bryll et al., 2003; Tsymbal et al., 2005) suggest that the ensemble of independently trained random feature models can indeed significantly improve test-time performance, as it enlarges the…”
Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS (2018, unknown): “We expect the improvement to increase with increasing diversity among ensemble members (Brown et al., 2005; DelSole et al., 2014 3.9 How do IMERG and ERA5 compare?”
Ensemble approaches for regression: A survey (2012, methodology): “For neural networks, the methods based on negative correlation (e.g., EENCL [Liu et al. 2000]) are particularly 10:16 J. Mendes-Moreira et al. appealing, due to their theoretical foundations [Brown et al. 2005b] and successful empirical results.”
Fast decorrelated neural network ensembles with random weights (2014, methodology): “An RVFL network can be defined as a SLFN model, Please cite this article in press as: M. Alhamdoosh, D. Wang, Fast decorrelated neural network ensembles with random weights, Inform.”
Ensemble learning: A survey (2018, methodology): “This is done by leading the base model to different convergence paths (Brown, Wyatt, & Ti ň o, 2005).”
Meta-learning for time series forecasting and forecast combination (2010, background): “One of the few publications dealing with diversity in a regression context is [9], where an error function e for training regression ensembles is introduced following the formula where ˆ y i denotes the prediction of the i th of M models, y the actual observation and ¯ˆ y the mean of the output of…”
Ensemble deep learning: A review (2021, methodology): “Ambiguity decomposition was given for single dataset based ensemble methods, later on, multiple dataset bias-variance-covariance decomposition was introduced in [27, 28, 29, 30] and is given as:”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, unknown): “In [106], NCL was employed for regression.”
A survey of multiple classifier systems as hybrid systems (2014, unknown): “[60] showed a func-”
Using Class Imbalance Learning for Software Defect Prediction (2013, background): “Ensemble learning algorithms have been shown to be able to combine strength from individual learners, and enhance the overall performance [25], [26].”
Learning from imbalanced data: open challenges and future directions (2016, background): “diversity is better explored from theoretical point of view in regression than in classification [6].”