Research And Design Of IDSS Based On SVM

Intelligent Decision Support System (IDSS) is a combination of Decision Support Systems and Artificial Intelligence. IDSS can effectively solve the problem of qualitative analysis combined with quantitative analysis, and those semi-structural and non-structural problems. Its application realms are extended, and its ability of solving problems is improved. Along with the unceasing development of the computer technology as well as the artificial intelligence technology, the intelligence decision support system will be researched and applied further, and will play a more and more vital role in many realms.Focused on assisting the decision-makers of real estate enterprise to make scientific decision, based on the In-depth analysis of real estate investment decision-making theory and procedures, and by making use of the advanced techniques of Data Warehouse, Data Mining, On-Line Analytical Processing and Support Vector Machine (SVM), this thesis designed a prototype system of Real Estate Investment Intelligent Decision Support System (REIIDSS). The system has important theory meaning and real-life value for reducing the risks of blindness and increasing the efficiency of decision.Firstly, by means of the basic theories and methods of IDSS, the collectivity structure of REIIDSS were designed in this thesis. And the data warehouse, the model subsystem and the knowledge-based subsystem were specifically analyzed and designed.Secondly, in allusion to the key steps of the real estate investment location decision-making, Support Vector Machine is used to establish the corresponding model, which can reduce the impacts from the experience and subjective awareness of decision-makers and increase the accuracy of location decisions.Finally, according to the characteristics of the real estate investment projects, SVM and multi-level fuzzy synthetic judgment are utilized to build up a Real Estate Investment Optimization Model. The model analyzes and quantifies some qualitative and fuzzy features, such as experience, opinion and viewpoint. It causes the scheme being established in the scientific foundation to ensure the science of the conclusion. At the same time, it is favor of SVM to handle those quantified features, and increases the efficiency of decision analysis.