A dynamic ensemble learning algorithm for neural networks

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–...

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Vydáno v:Neural computing & applications Ročník 32; číslo 12; s. 8675 - 8690
Hlavní autoři: Alam, Kazi Md. Rokibul, Siddique, Nazmul, Adeli, Hojjat
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.06.2020
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Ensemble learning
Heart diseases
Image Processing and Computer Vision
Machine learning
Mushrooms
Neural networks
Original Article
Probability and Statistics in Computer Science
Pruning
Soybeans
Backpropagation algorithm
Pruning algorithms
Neural network ensemble
Constructive algorithms
Negative correlation learning
ISSN:0941-0643, 1433-3058
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Shrnutí: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04359-7