| dc.description.abstract | I Ching is the wisdom of Chineses ancient and contains extensice and profound logical system. There has deep implied meaning in I Ching. I Ching uses abstract symbols to present everything in the world. Bagua that created by Fu Xi is the basic of I Ching, and including Qián (☰), Duì (☱), Lí (☲), Zhèn (☳), Xùn (☴), Kǎn (☵), Gèn (☶) and Kūn (☷). The connotations of I Ching are Philosophy, Image and Number and the generalization of Bagua is the most important part of Image and Number. Image and Number will be obtained from practise divination, and collocate with things of generalization to acquire the answer of divination. Howerer, everyone has diverse explaination and different understanding of I Ching, and there did not have an effective method to specifically refer things to Bagua. Hence, this research uses supervised learning of machine learning to solve the problems of generalization of Bagua. Everything in the world have been grouped by seven subject including human, situation, body, disease, object and building, and feature attributes and property values of every subject have been found. This research utilized the feature attributes and property values to establish datasets of seven subjects. C4.5, k- nearest neighbors algorithm and support vector machine have been applied to produce different classifiers and compare the performances of classifiers. After comparing original datasets and resampling dataset, kNN presented the most efficient accuracy of classification among C4.5, kNN and SVM. This research provides an empirical origination of generalization of Bagua. The most efficient accuracy of object is closed to 90%. This result can improve the practicability of Bagua divination. | en_US |