<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Constructive learning algorithms are an efficient way to train feedforward neural networks. Some of their features, such as the automatic definition of the neural network (NN) architecture and its fast training, promote their high adaptive capacity, as well as allow for skipping the usual pre-training phase, known as model selection. However, such advantages usually come with the price of lower accuracy rates, when compared to those obtained with conventional NN learning approaches. This is, perhaps, the reason for conventional NN training algorithms being preferred over constructive NN (CoNN) algorithms. Aiming at enhancing CoNN accuracy performance and, as a result, making them a competitive choice for machine learning based applications, this paper proposes the use of functionally expanded input data. The investigation described in this paper considered six two-class CoNN algorithms, ten data domains and seven polynomial expansions. Results from experiments, followed by a comparative analysis, show that performance rates can be improved when CoNN algorithms learn from functionally expanded input data.</p></abstract>

Constructive learning algorithms are an efficient way to train feedforward neural networks. Some of their features, such as the automatic definition of the neural network (NN) architecture and its fast training, promote their high adaptive capacity, as well as allow for skipping the usual pre-training phase, known as model selection. However, such advantages usually come with the price of lower accuracy rates, when compared to those obtained with conventional NN learning approaches. This is, perhaps, the reason for conventional NN training algorithms being preferred over constructive NN (CoNN) algorithms. Aiming at enhancing CoNN accuracy performance and, as a result, making them a competitive choice for machine learning based applications, this paper proposes the use of functionally expanded input data. The investigation described in this paper considered six two-class CoNN algorithms, ten data domains and seven polynomial expansions. Results from experiments, followed by a comparative analysis, show that performance rates can be improved when CoNN algorithms learn from functionally expanded input data.

Enhancing Constructive Neural Network Performance Using Functionally Expanded Input Data

Department of Computer Science, UFSCar, Rodovia Washington Luís, km 235, São Carlos-SP, Brazil

Department of Computer Science, UFSCar & FACCAMP, São Carlos & Campo Limpo Paulista - SP, Brazil

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Constructive learning algorithms are an efficient way to train feedforward neural networks. Some of their features, such as the automatic definition of the...

Enhancing Constructive Neural Network Performance Using Functionally Expanded Input Data

Publié en ligne: 10 mars 2016

Pages: 119 - 131

DOI: https://doi.org/10.1515/jaiscr-2016-0010

Mots clés
Constructive neural networks, Functional link artificial neural networks, Functionally expanded input data

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Enhancing Constructive Neural Network Performance Using Functionally Expanded Input Data

Publié en ligne: 10 mars 2016

Pages: 119 - 131

DOI: https://doi.org/10.1515/jaiscr-2016-0010

Mots clésConstructive neural networks, Functional link artificial neural networks, Functionally expanded input data

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Constructive neural networks, Functional link artificial neural networks, Functionally expanded input data