A Multiclassifier Approach for Drill Wear Prediction

Díez Oliván, Alberto and Carrascal, Alberto (2012). A Multiclassifier Approach for Drill Wear Prediction. In: "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.

Description

Title: A Multiclassifier Approach for Drill Wear Prediction
Author/s:
  • Díez Oliván, Alberto
  • Carrascal, Alberto
Item Type: Presentation at Congress or Conference (Article)
Event Title: Machine Learning and Data Mining in Pattern Recognition 8th International Conference, MLDM 2012
Event Dates: July 13-20, 2012
Event Location: Berlin, Germany
Title of Book: Machine Learning and Data Mining in Pattern Recognition 8th International Conference, MLDM 2012. Proceedings
Date: 2012
ISBN: 978-3-642-31537-4
Volume: 7376
Subjects:
Freetext Keywords: Classification, multiclassifier, drill wear prediction, pattern identification
Faculty: E.T.S.I. Industriales (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 46994
DC Identifier: http://oa.upm.es/46994/
OAI Identifier: oai:oa.upm.es:46994
DOI: 10.1007/978-3-642-31537-4_48
Official URL: https://link.springer.com/chapter/10.1007/978-3-642-31537-4_48
Deposited by: Memoria Investigacion
Deposited on: 23 Jun 2017 14:27
Last Modified: 23 Jun 2017 14:27
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