TY - GEN
T1 - A multiclassifier approach for drill wear prediction
AU - Diez, Alberto
AU - Carrascal, Alberto
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Classification
KW - drill wear prediction
KW - multiclassifier
KW - pattern identification
UR - https://www.scopus.com/pages/publications/84864917127
U2 - 10.1007/978-3-642-31537-4_48
DO - 10.1007/978-3-642-31537-4_48
M3 - Conference contribution
AN - SCOPUS:84864917127
SN - 9783642315367
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 630
BT - Machine Learning and Data Mining in Pattern Recognition - 8th International Conference, MLDM 2012, Proceedings
T2 - 8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012
Y2 - 13 July 2012 through 20 July 2012
ER -