A Comparison of Different Classification Techniques to Determine the Change Causes in Hotelling's T2 Control Chart

To detect out‐of control situations using multivariate control charts is relatively easy. However, the determination of the change causes is more difficult. In the last years the application of classification techniques to analyze the out‐of‐control signals has been proposed. These proposals include increasingly sophisticated methods but it is not clear, if there is one, which is the most powerful. In this paper we have simulated different scenarios varying the correlation structure and shift type, to test the performance of Linear Discriminant Analysis, Classification Trees, Neural Networks and Boosting Trees with the main aim to analyze whether or not using more complex classification methods is worthy. Results show that although the performance of different methods depends on the correlation structure and the class of change, under the more feasible situations Boosting shows the best performance.