基于NSGA-Ⅱ的非平衡制造数据关键质量特性识别

针对非平衡产品制造数据关键质量特性(critical to quality characteristics,CTQs)识别,提出基于NSGA-Ⅱ的特征选择算法.首先,在分类错误率和特征子集大小基础上,针对数据非平衡性,引入第Ⅱ类错误率度量质量特性子集的重要性.接着,应用多目标进化算法NSGA-Ⅱ最小化以上三个度量标准,得到非支配解集.最后,引入理想点法从非支配解集中选择最佳调和解,得到CTQ集.算例结果表明,所提算法能够得到较高分类精度,同时有效降低第Ⅱ类错误率与CTQ集大小,说明了算法的有效性.

Critical to quality characteristics identification for imbalanced production data based on NSGA-II

To select critical to quality characteristics (CTQs) for imbalanced production data, a feature selection algorithm based on NSGA-II is proposed. Firstly, to solve the problem of data imbalance, type II error is introduced to measure the importance of quality characteristics subset in addition to classification error and feature subset size. Secondly, NSGA-II, a multi-objective evolutionary algorithm, is applied to minimize the three metrics above, and a non-dominated solution set is acquired. Finally, the ideal point method is adopted to obtain the best compromise solution (CTQ set) from the non-dominated solution set. Experimental results illustrate that the proposed algorithm can obtain high classification accuracy, and in the meantime, effectively reduce type II error and CTQ set size, which shows the efficiency of the proposed algorithm.