A Method for Cigarette Sensory Quality Evaluation Based on Cost-sensitive Learning

Arming at the cost-sensitive problems in cigarette sensory evaluation, Cost-Sensitive Back-Propagation Neural Networks (CSBPNN) was employed in this paper to deal with the problems derived from cigarette sensory evaluation. In order to verify the effectiveness of our methodology, the cost matrix was obtained based on production practice and the comparative experimental study was carried out by using dataset from a tobacco company. The experimental results indicated that our methods have a significant advantage on total misclassification cost, high cost label recognition rate and average classification accuracy when compared with the cost-insensitive methods.