Small Dataset Modeling and Application of Plant Medicine Extraction

• Published in: Cognitive Systems and Signal Processing Vol. 1006; pp. 381 - 392
• Main Authors: Liu, Boyan, Chen, Juan, Dong, Cuiying
• Format: Book Chapter
• Language: English
• Published: Singapore Springer 2019; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Plant medicine extraction; Small dataset modeling; Virtual sample generation
• ISBN: 9789811379857; 9811379858
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-13-7986-4_34

Intelligent modeling is an effective method to build prediction model of the plant medicine with ultrasonic extraction. However, there are obstacles when obtaining lots of data by the plant medicine extraction experiments, and small dataset will result in a model with low accuracy and poor generalization ability, which has a great influence on it. This paper proposes a novel virtual sample generation (VSG) approach based on Response Surface Methodology (RSM) and Extreme Learning Machine (ELM) algorithm, selecting through Cuckoo Search (CS) algorithm. The new prediction model is constructed based on ELM with the virtual sample dataset generated by this method and the original small sample dataset. The performance of the model is verified via the case of extracting the active ingredients, liquiritin, from liquorice by dual-frequency ultrasound. The experiment results show that the model established by the method proposed in this paper can significantly reduce the prediction error and improve the accuracy of the model, which provides a certain theoretical basis and reference for the industrialization of the active ingredients extraction of plant medicine.