期刊名称：Journal of Artificial Intelligence and Soft Computing Research
电子版ISSN：2083-2567
出版年度：2016
卷号：6
期号：2
页码：119-131
DOI：10.1515/jaiscr-2016-0010
出版社：Walter de Gruyter GmbH
摘要：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.
关键词：Constructive neural networks ; Functional link artificial neural networks ; Functionally expanded input data
