Automated Classification of Metamodel Repositories: A Machine Learning Approach
Automated Classification of Metamodel Repositories: A Machine Learning Approach
复制标题
元模型存储库的自动分类：一种机器学习方法
DOI：
10.1109/models.2019.00011
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Ludovico Iovino
中科院分区：
文献类型：
--
作者：
P. T. Nguyen;Juri Di Rocco;D. D. Ruscio;A. Pierantonio;Ludovico Iovino
元模型库的手动分类方法需要训练有素的人员，并且结果通常受到人类感知的主观性的影响。因此，自动化的元模型分类是非常可取和严格的。在这项工作中，机器学习技术已被用于元模型自动分类。特别是，实现前馈神经网络的工具被引入到分类元模型。对555个元模型数据集的实验评估表明，该技术允许从手动分类数据中学习，并以相当高的预测率有效地对传入的未标记数据进行分类：最佳性能为成功率95.40%，精度0.945，召回率0.938，F1得分0.942。
Manual classification methods of metamodel repositories require highly trained personnel and the results are usually influenced by the subjectivity of human perception. Therefore, automated metamodel classification is very desirable and stringent. In this work, Machine Learning techniques have been employed for metamodel automated classification. In particular, a tool implementing a feed-forward neural network is introduced to classify metamodels. An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate: the best performance comprehends 95.40% as success rate, 0.945 as precision, 0.938 as recall, and 0.942 as F1 score.