The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Evolutionary ensembles with negative correlation learning
Abstract
Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.
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Citations
Cites
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Boosting and Other Ensemble Methods
Drucker, H., Cortes, C., Jackel, L. D., LeCun, Y., & Vapnik, V. (1994) · Neural Computation · doi:10.1162/neco.1994.6.6.1289 · Cited by 357
Cited by
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Zhang, C., Lim, P., Qin, A. K., & Tan, K. C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582798 · Cited by 870
Ensemble approaches for regression: A survey
Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379786 · Cited by 667
Metaheuristic design of feedforward neural networks: A review of two decades of research
Ojha, V. K., Abraham, A., & Snášel, V. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.01.013 · Cited by 606
Generalizing Surrogate-Assisted Evolutionary Computation
Lim, D., Jin, Y., Ong, Y.-S., & Sendhoff, B. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2027359 · Cited by 484
Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications
Li, X., Epitropakis, M. G., Deb, K., & Engelbrecht, A. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2638437 · Cited by 339
Metadata
- Authors
- Higuchi, T., Yao, X., & Liu, Y.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2000
- Volume
- 4
- Issue
- 4
- Pages
- 380-387
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2145833756
- Cited by
- 445
- References
- 3
- FWCI
- 10.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- Higuchi, T., Yao, X., & Liu, Y. (2000). Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/4235.887237
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