Study On Knowledge Acquisition And Application Based On Rough Set Theory Aimed At Syndrome Differentiation In TCM

With the rapid development of modern science and technology, intelligent information processing has become hot point in many research fields, thus the corresponding technologies have become the urgent supporting power for the modernization of traditional Chinese medicine (TCM). While the modernization of diagnosis in TCM is one of the important facets of the modernization of TCM, intelligent diagnosis in TCM appears to be the perfect research entry for combing intelligent technologies with diagnostic technologies in TCM, and its core problem and key technology lie in the intelligent syndrome differentiation in TCM (SDTCM)(syndrome differentiation is an unique concept of TCM).Based on the earlier research activities, it has been emerged that the key point of intelligent diagnosis in TCM may be related to knowledge processing including knowledge representation, knowledge acquisition, knowledge discovery and knowledge utilization, etc., and the difficult problems occurred are always the important research topics in the area of artificial intelligence. Fortunately, a series of advanced intelligent technologies based on soft computing have brought great opportunity for solving these problems, and they, in return, also can attain new enlightenment and enrich their research content and harvest.In this dissertation, the state-of-the-art of soft computing for the intelligent SDTCM as well as its fundamental methods and difficulties are studied and analyzed. The other research topics involve in representing uncertain knowledge, designing information model by concluding and simulating experiences of human experts, discovering knowledge from large data, and reducing large data, etc. The purpose of this dissertation is to provide some new ideals, methods and technologies for the realization of the computing in SDTCM, and to propose several new resolutions for the related hard problems in the area of artificial intelligence.The main content is as follows.In the first chapter, the background and the significance of the research are interpreted and the research activities and applications about rough set theory are reviewed. It is also pointed out that the critical point of the intelligent diagnosis in TCM is the realization of the computing of SDTCM, and the main corresponding problems lie in knowledge processing including knowledge acquisition, knowledge discovery and knowledge exploiting. As for the rough set theory, the state-of-the-art is outlined, and the research works based on it are analyzed involving knowledge acquisition, attribution reduct as well as the application in the intelligent SDTCM.In the second chapter, first, the fundamental concepts, principles and methods of SDTCM are introduced. Second, the advantages and difficulties of soft computing in the research on the intelligent SDTCM are analyzed; Finally, the state-of-the-art of the intelligent SDTCM is interpreted in detail: 1) the diagnostic methods and clustering methods of syndromes for SDTCM based on fuzzy set theory are concluded ; 2) the stat-of-the-art, fundamental viewpoints, general methods, existing problems with corresponding resolution ideas of the applications based on neural network for SDTCM are analyzed, and our initial idea and research works for this area are introduced; 3) the general steps of the practical methods based on rough set theory for SDTCM are reviewed and concluded; 4) the research works for the fusion of multi-technology are also introduced in brief.Aiming at the problem about knowledge acquisition, which is a neck problem when developing an intelligent system, in the third and fourth chapters, two kinds of human experts' thinking ways, the focusing mechanism and the hierarchical clustering mechanism, are respectively examined, and the limitations of the existed approaches to extracting classification rules by modeling these two thinking ways are carefully examined. The focusing mechanism is clearly represented by three ordered processes: exclusion process, discrimination process and combination process. One of the main limitations of the approaches existed is that they can only do discrimination between two classes, since they exploit coverage criteria in the discrimination process. The point departure for the improvement is: if exploiting accuracy criteria in the discrimination process, then the discrimination process may be done among many classes. Furthermore, they can be combined with the methods for computing attribute reducts. For the focusing mechanism, the algorithms REFM is proposed in the third chapter. For the hierarchical clustering mechanism, the algorithm REHC is proposed in the fourth chapter.To challenge the NP-hard problem computing the total attribute reducts (including the total minimal attribute reducts) in large information systems, in the fifnth chapter, a series of equivalent form of discernibility function are examined; then an important and novel concept, i.e. reduct disernibility graph (RDG), is proposed; furthermore, the complete theory for computing attribute reducts based on RDG as well as the corresponding algorithm CARRDG are proposed and interpreted; the effectiveness and completeness of the algorithm CARRDG are also proved; at last the results of the experiment on six typical UCI data sets show: the algorithm CARRDG can compute total attribute reducts within 0.5 seconds for the general information systems, and within several minutes and more than 90% trim rate for the large information systems even with 20000 objects. It should be noted that although the algorithm CARRDG is proposed for the special problem, in fact it solves, however, the problem of the transformation and simplification of logic expressions from conjunctive normal form to disjunctive normal form, so it has wide variety of application fields, and it maight also open a new window for solving the combination exposition problems in the real-world problems.In the sixth chapter, the tentative idea for the future structure of the SDTCM system with learning mechanism is proposed, and the application means and the significance of our research fruits in the structure are interpreted. At last, the main research work and the existing problems of this dissertation are concluded and the future of the research is also prospected.