Improving learning accuracy by using synthetic samples for small datasets with non-linear attribute dependency

Small-data problems are commonly encountered in the early stages of a new manufacturing procedure, presenting challenges to both academics and practitioners, as good performance is difficult to achieve with learning models when there is a lack of sufficient data. Virtual sample generation (VSG) has been shown to be an effective method to overcome this issue in a wide range of studies in various fields. Such works usually assume that the relations among attributes are independent of each other, and produce synthetic data by using sample distributions of these. However, the VSG technique may be ineffective if the real data has interrelated attributes. Therefore, this research provides a novel procedure to generate related virtual samples with non-linear attribute dependency. To construct a relational model between the independent and dependent attributes, we employ gene expression programming (GEP) to find the most suitable mathematical model. One practical dataset and three real UCI datasets are presented in this paper to verify the effectiveness of the proposed method, and the results show that the proposed approach has better learning accuracy with regard to a back-propagation neural (BPN) network than that of the well-known mega-trend-diffusion (MTD) and the multi regression analysis (MRA) approaches.