AUTOMATION OF GENERALIZED ADDITIVE NEURAL NETWORKS FOR PREDICTIVE DATA MINING
AUTOMATION OF GENERALIZED ADDITIVE NEURAL NETWORKS FOR PREDICTIVE DATA MINING
复制标题
用于预测数据挖掘的广义可加神经网络的自动化
DOI：
--
复制
发表时间：
2011
影响因子：
2.8
通讯作者：
J. Toit
中科院分区：
文献类型：
--
作者：
D. D. Waal;J. Toit
为了使新技术从实验技术迈向主流技术，需要创建工具来促进在设想的应用领域中使用所开发的技术。广义加性神经网络提供了一个有吸引力的框架，显示在预测数据挖掘领域的承诺。然而，这种网络的构建是非常耗时和主观的，因为它依赖于用户来解释部分残差图并对神经网络架构进行更改。对于这种技术被接受为预测数据挖掘领域的一个严重的建模选项，构建过程需要自动化，使用该技术的好处必须清楚地阐明。本文展示了如何使用智能搜索来取代主观的人类判断与客观的标准，使广义加性神经网络的建模有吸引力的选择。
For a new technology to make the step from experimental technology to mainstream technology, tools need to be created to facilitate the use of the developed technology in the envisaged application area. Generalized additive neural networks provide an attractive framework that shows promise in the field of predictive data mining. However, the construction of such networks is very time consuming and subjective, because it depends on the user to interpret partial residual plots and to make changes in the neural network architecture. For this technology to be accepted as a serious modeling option in the field of predictive data mining the construction process needs to be automated and the benefits of using the technique must be clearly illuminated. This article shows how intelligent search may be used to replace subjective human judgment with objective criteria and make generalized additive neural networks an attractive option for the modeler.