Classification ensembles for shaft test data : Empirical evaluation

Abstract

A-scans from ultrasonic testing of long shafts are complex signals. The discrimination of different types of echoes is of importance for non-destructive testing and equipment maintenance. Research has focused on selecting features of physical significance or exploring classifier like Artificial Neural Networks and Support Vector Machines. This paper confirms the observation that there seems to be uncorrelated errors among the variants explored in the past, and therefore an ensemble of classifiers is to achieve better discrimination accuracy. We explore the diverse possibilities of heterogeneous and homogeneous ensembles, combination techniques, feature extraction methods and classifiers types and determine guidelines for heterogeneous combinations that result in superior performance.