Features extraction and analysis for classifying causable patterns in control charts

In this work we introduce a multi-resolution analysis approach based on discrete cosine transform (DCT) that overcomes the problems associated with MRWA. We also verify that the classification rates of shift, trend, and cyclic causable patterns using multi-resolution DCT (MRDCT) features are higher than those obtained using MRWA features. Furthermore, the computational requirements for MRDCT are notably less than those needed for MRWA. Artificial neural network (ANN) classifier was used with both feature extraction methods.