Manual classification methods of metamodel reposi- tories require highly trained personnel and the results are usually influenced by subjectivity of human perception. Therefore, auto- mated 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.
Automated Classification of Metamodel Repositories: A Machine Learning Approach
Phuong T. Nguyen
;Juri Di Rocco
;Davide Di Ruscio
;Alfonso Pierantonio
;ludovico iovino
2019-01-01
Abstract
Manual classification methods of metamodel reposi- tories require highly trained personnel and the results are usually influenced by subjectivity of human perception. Therefore, auto- mated 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.Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.