A dynamic ensemble learning algorithm for neural networks

Kazi Md. Rokibul Alam, N Siddique, Hojjat Adeli

Research output: Contribution to journalArticlepeer-review

318 Citations (Scopus)
202 Downloads (Pure)


This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive–pruning strategy, and different training samples for individual NN’s learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble; (2) maintaining accuracy and diversity of NNs at the same time; and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.
Original languageEnglish
Pages (from-to)8675–8690
Number of pages16
JournalNeural Computing and Applications
Issue number12
Early online date29 Jul 2019
Publication statusPublished (in print/issue) - 30 Jun 2020


  • Neural network ensemble
  • Backpropagation algorithm
  • Negative correlation learning
  • Constructive algorithms
  • Pruning algorithms


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