A PSO based virtual sample generation method for small sample sets: Applications to regression datasets

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• 作者：Zhong-Sheng Chen^a ; Bao Zhu^b ; Yan-Lin He^a ; ^{heyl@mail.buct.edu.cn} ; Le-An Yu^b
• 关键词：Small sample set ; Virtual sample generation ; Information expanded ; Regression
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：59
• 期：Complete
• 页码：236-243
• 全文大小：1143 K
• 卷排序：59

文摘

In the early period of process industries, it is an intractable challenge to build an accurate and robust forecasting model using the collected scared samples. The information derived from small sample sets is unreliable and weak. Thus, the models established based on the small sample sets are inefficient. Virtual sample generation (VSG) is a promising technology which can be used to generate plenty of new virtual samples by the information acquired from small sample sets, aiming at improving the accuracy of forecasting models. To capture the tendency of the raw sample set and reduce information gaps among individuals, an information-expanded function based on triangular membership (TMIE) is developed to asymmetrically expand the domain range in each attribute in this paper. A novel particle swarm optimization based VSG (PSOVSG) approach is proposed to iteratively generate the most feasible virtual samples over the search-space. The effectiveness of PSOVSG is tested against other three methods of VSG over two real cases: multi-layer ceramic capacitors (MLCC) and purified Terephthalic acid (PTA). The simulation results show the proposed PSOVSG achieves better performance than other methods.