A novel mega-trend-diffusion for small sample
Bao Zhu, Zhong-Sheng Chen, Yu Le ' an · 2016
Process modeling, optimization and control methods based on data-driven attract attention to both academic community and business circles in terms of its research domains and applications. Even in Big Data era, small sample problems cannot be ignored. In view of the difficulty of obtaining high learning accuracy with small-sample-set using traditional modeling methods, such as artificial neural networks (ANNs), extreme learning machine (ELMs), etc., a novel technology of multi-distribution mega-trend-diffusion (MD-MTD) is proposed to improve the learning accuracy of small-sample-set. The mega-trend-diffusion (MTD) is employed to estimate the acceptable range of the attribution of small sample. The uniform distribution and triangular distribution are added based on MTD to describe data characteristics, which are used to generate virtual samples and fill information gaps among observations in small sample. A benchmarking function is utilized to generate benchmarking samples under the orthogonal test and inhomogeneous sample test in order to verify the reasonability and effectiveness of the MD-MTD, and two industrial real-world datasets include MLCC and PTA are used to further confirm the practicability of the MD-MTD. The results of the validation tests manifest that the proposed MD-MTD can improve the learning accuracy of more than 8% for small sample.