A Multiclassifier Approach for Drill Wear Prediction

Díez Oliván, Alberto y Carrascal, Alberto (2012). A Multiclassifier Approach for Drill Wear Prediction. En: "Machine Learning and Data Mining in Pattern Recognition 8th International Conference, MLDM 2012", July 13-20, 2012, Berlin, Germany. ISBN 978-3-642-31537-4. pp. 617-630. https://doi.org/10.1007/978-3-642-31537-4_48.

Descripción

Título: A Multiclassifier Approach for Drill Wear Prediction
Autor/es:
  • Díez Oliván, Alberto
  • Carrascal, Alberto
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Machine Learning and Data Mining in Pattern Recognition 8th International Conference, MLDM 2012
Fechas del Evento: July 13-20, 2012
Lugar del Evento: Berlin, Germany
Título del Libro: Machine Learning and Data Mining in Pattern Recognition 8th International Conference, MLDM 2012. Proceedings
Fecha: 2012
ISBN: 978-3-642-31537-4
Volumen: 7376
Materias:
Palabras Clave Informales: Classification, multiclassifier, drill wear prediction, pattern identification
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them according to three different methods: confidence voting, weighted voting and majority voting. To illustrate its applicability in a real problem, the drill wear detection in machine-tool sector is addressed. In this study, the accuracy obtained by each isolated classifier is compared with the performance of the multiclassifier when characterizing the patterns of interest involved in the drilling process and predicting the drill wear. Experimental results show that, in general, false positives obtained by the classifiers can be slightly reduced by using the multiclassifier approach.

Más información

ID de Registro: 46994
Identificador DC: http://oa.upm.es/46994/
Identificador OAI: oai:oa.upm.es:46994
Identificador DOI: 10.1007/978-3-642-31537-4_48
URL Oficial: https://link.springer.com/chapter/10.1007/978-3-642-31537-4_48
Depositado por: Memoria Investigacion
Depositado el: 23 Jun 2017 14:27
Ultima Modificación: 23 Jun 2017 14:27
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