Identification of Critical-to-quality Characteristic in Complex Products Based on the Adaptive-Lasso Method

Abstract

Targeting the problem of the redundancy in complex product quality characteristics, the Adaptive-Lasso method is introduced into the identification of Critical-to-quality Characteristic. By using the Adaptive-Lasso method to filter variables, reduce the dimensions of the original quality data sample set, and obtain the order of the correlation between the quality Characteristics in the sample set and the quality category, the quality Characteristics with the highest classification correct ratio are selected to form the Critical-to-quality Characteristic subset. On this basis, the classification correct ratio of the selected Characteristic subset is tested by using the support vector machine. The example shows that compared with the traditional ReliefF method and Lasso method, this method can effectively remove the irrelevant and redundant features in the original data set to achieve the purpose of identifying the Critical-to-quality Characteristic.