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A Multiclassifier Approach for Drill Wear Prediction

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7376)

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.

Keywords

  • Classification
  • multiclassifier
  • drill wear prediction
  • pattern identification

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Diez, A., Carrascal, A. (2012). A Multiclassifier Approach for Drill Wear Prediction. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_48

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  • DOI: https://doi.org/10.1007/978-3-642-31537-4_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31536-7

  • Online ISBN: 978-3-642-31537-4

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