Abstract
The popularity of herbal medicines has greatly increased in worldwide countries over recent years. Herbal formula is a form of traditional medicine where herbs are combined to heal patient to heal faster and more efficiency. Herbal formulae can be divided into one or more therapeutic categories. The categories of a formula are usually based on decision from a group of experts. To support experts for classifying a formula, the normalized score centroid-based, is proposed for multi-label herbal formulae classification. The centroid-based classifier with more advanced term weight scheme is used. The normalized scores are calculated. The maximum number and cutoff point are set to adjust the decision for multi-label herbal formulae. The experiment is done using a mixed data set of herbal formulae collected from the Natural List of Essential Medicine and the list of common household remedies for traditional medicine. Moreover, a set of well-known commercial products are used for evaluating the effectiveness of the proposed method. From the results, the normalized score centroid-based classifier is an efficient method to classify multi-label herbal formulae. Its performance is depended on the set values of the maximum category and the cutoff point.
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Lertnattee, V., Chomya, S., Sornlertlamvanich, V. (2013). Using a Normalized Score Centroid-Based Classifier to Classify Multi-label Herbal Formulae. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_12
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DOI: https://doi.org/10.1007/978-3-642-44949-9_12
Publisher Name: Springer, Berlin, Heidelberg
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