Evolutionary ensembles with negative correlation learning

By Yong Liu, Xin Yao, Tetsuya Higuchi. IEEE Transactions on Evolutionary Computation, 2000

478 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6b999b4f396a9e98f00fdf2447a49ddfb386506b

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Citations in the corpus

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, background): “To achieve the tradeoff when designing an ensemble of MLPs, negative correlation learning has been proposed (Liu and Yao 1999; Liu et al. 2000; Islam et al. 2003).”
Neuroevolution (2010, background): “The fifth approach is to evolve an ensemble of neural networks to solve the task together, instead of a single network [11].”
Ensemble approaches for regression: A survey (2012, methodology): “trees yes / ? EENCL [1] ANN yes / yes CNNE [45] ANN yes / yes Coop.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, methodology): “In particular, since it is almost impossible to know in advance which approximation technique best suits the optimization problem at hand, we consider a synergy of diverse approximation methods through the use of ensemble models to generate reliable accurate predictions across problems of differing ”
How many hidden layers and nodes? (2009, background): “The problem can be formulated as a search task in the architecture space where each point represents an architecture (Liu et al. 2000).”
A constructive algorithm for training cooperative neural network ensembles (2003, methodology): “, use cross-validation, while other do not, such as EENCL [31].”
Fast decorrelated neural network ensembles with random weights (2014, background): “It has been noticed that the proposed approach in [13] can automatically determine an optimal size of ensemble by thoroughly exploring the ensemble hypotheses space using NCL.”
Managing Diversity in Regression Ensembles (2005, result): “(2001), classification problems McKay and Abbass (2001), and time-series prediction Liu (1998). It has consistently demonstrated significant performance improvements over a normal ensemble system, showing very competitive results with other techniques like Mixtures of Experts, Bagging, and Boosting ”
Review of soft sensor methods for regression applications (2016, methodology): “In [98], an evolutionary ensemble learning using NN and based on negative correlation learning was presented.”
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics (2017, methodology): “Here, we employ the idea from NCL [61], which uses the correlation between the outputs of ensemble members to define the diversity [44] as follows:”
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, methodology): “EENCL is evolutionary ensembles with negative correlation learning presented in [62].”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, background): “Moreover, different FNNs were allowed learn different parts of training data and the best (according to fitness) were selected for ensemble [319].”
Diversity in genetic programming: an analysis of measures and correlation with fitness (2004, background): “It should be mentioned that diversity is studied in o ther areas of evolutionary algorithms (neural network ense mbl s [31] for example) but is out of the scope of this paper.”
An ensemble of neural networks for weather forecasting (2004, background): “It has the advantage of being easy to understand and implement [1, 15] and can be shown not to increase the expected error [1, 23, 24].”