Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
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Computers in Industry - Special issue: E-maintenance
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Knowledge and Information Systems
Data Mining for Burr Detection (in the Drilling Process)
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HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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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.