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Title: A classifier for herbal supplements and proprietary medicines with multilingual descriptions
Authors: Yang, Can
Keywords: DRNTU::Engineering::Computer science and engineering::Data
Issue Date: 2019
Abstract: Herbal supplements and proprietary medicines of traditional Chinese medicine (TCM), have been receiving wide attention from the public because of their perceived unique effects on chronic and consumptive diseases. They contain natural ingredients with complex chemical constituents which are difficult to identify, characterize or standardize. This project aims at reviewing related machine learning methods and to find the appropriate classifiers for herbal supplements and proprietary medicines from TCM. My experiments were conducted by analyzing the data extracted from TCM database and Hong Kong Baptist University (HKBU) using python and state-of-the-art machine learning and data science libraries. In particular, latent Dirichlet allocation (LDA) and multi-label classification method were applied as unsupervised and supervised learning methods to build my proposed models.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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