Integral DOE and MANOVA techniques for classification feature selection: using solder joint defects as an example

摘要

This study presents a novel approach for feature selection using an integrated DOE and MANOVA technique to classify solder joint defects for print circuit boards (PCBs). The main selection procedure includes three stages. The first stage adopts a single feature variable selection algorithm to eliminate poorly discriminated feature variables. The second stage, Plackett-Burman (PB) resolution III design, is then constructed to select the remaining feature variables. The MANOVA technique is then used to calculate the Pillai statistic as the response to the PB design of experiment, and statistical analysis is then executed to obtain the optimal multiple feature variables for multiple groups. The discriminate function classifier is used to evaluate the classification results. The experimental analysis results show that the proposed analysis procedure can acquire an optimum subset of features for classification.