Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Fuzzy information engineering: a guided tour of applications
Fuzzy information engineering: a guided tour of applications
ACM SIGKDD Explorations Newsletter
Complete Cross-Validation for Nearest Neighbor Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
A self-learning expert system for diagnosis in traditional Chinese medicine
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Traditional Chinese Medicine (TCM) is one of the most important complementary and alternative medicines. Due to the subjectivity and fuzziness of diagnosis in TCM, quantitative model or methods are needed to facilitate the popularization of TCM. In this article, a novel quantitative method for syndrome differentiation based on BNs is proposed. First the symptoms are selected by a novel mutual information based symptom selection algorithm (MISS) and then the mapping relationships between the selected symptoms and key elements are constructed. Finally, the corresponding syndromes are output by combining the key elements. The results show that the diagnostic model obtains relative reliable predictions of syndrome, and its average predictive accuracy rate reach 91.68%, which testifies that the method we proposed is feasible and effective and can be expected to be useful in the modernization of TCM.