© 2016 IEEE. Selecting an adequate machine learning model, e.g. for feature selection or classification, is a very important task in developing machine learning applications. In order to perform an adequate selection, statistic tests are introduced by several approaches but some of them are hard to reproduce in different case studies due to the lack of a systematic application procedure. This work presents a methodological framework based on statistic tests, either parametric or non-parametric, to compare multiple machine learning models for solving a specific problem. The procedure first aims to detect which feature selection method is the best for each machine learning based model, and then such models are compared using the previous results. A real world problem for fault detection in rotating machinery is studied to illustrate the application of the proposed methodological framework, using the accuracy in classification as the performance measure.
|Translated title of the contribution||Un marco metodológico que utiliza pruebas estadísticas para comparar modelos basados en el aprendizaje automático aplicados al diagnóstico de fallos en máquinas rotativas.|
|Original language||English (US)|
|State||Published - 23 Mar 2017|
|Event||2016 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2016 - Proceedings - |
Duration: 23 Mar 2017 → …
|Conference||2016 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2016 - Proceedings|
|Period||23/03/17 → …|