Using an attribute conversion approach for sample generation to learn small data with highly uncertain features
针对新产品开发中因数据量小且特征高度不确定导致的机器学习预测不准问题，提出一种基于模糊理论的虚拟样本生成方法，通过属性转换生成样本，帮助工程师更准确地推断制造参数。
Accelerating new product development has become an important marketing strategy for manufacturers who are competing globally. However, this may lead to the small data learning issue. Although machine learning algorithms are used to extract knowledge from training samples, algorithms may not output satisfactory predictions when training sizes are small. This paper provides a real case of a TFT-LCD (thin film transistor liquid crystal display) maker when a new strengthened cover glass is developed using chemical processes. With very little prior experience about the processes involved, engineers attempted to improve the yield rates by determining the parameters from a few pilot-run data. However, owing to the fact that the processes were different from those required to make TFT-LCD panels, the highly uncertain characteristics of the processes led to the use of two virtual sample generation (VSG) approaches, bootstrap aggregating (bagging) and the synthetic minority over-sampling technique, from which unsatisfactory results were obtained. Accordingly, this study was used to develop a systematic VSG method based on fuzzy theory to tackle the learning issue. The experimental results show that support vector regressions built with training sets containing the proposed samples present more precise predictions and thus can help engineers infer more correct manufacturing parameters.