Research On Intelligent Decision Techniques Based On Optimized Case-Based Reasoning

How to optimize the managerial process of enterprises and social organizations and improve its intelligence has been a key issue in terms of both theory and practice. In modern management practice, the system is becoming increasingly larger, with more constraint conditions, more serous non-linear problems, and complexity of the surroundings, which add to the difficulty in the optimization of enterprise systems.In order to deal with these problems, an original Optimized Case-based Reasoning--Intelligent Decision Technique (OCBR-IDT) is presented in this paper, which touches upon Management Science, Artificial Intelligence (AI), Operation Research and Decision Science, et al academic fields, and it combines optimization theory (classic optimization and modern heuristic optimization technique) with decision technique. In detail, this paper includes:1. On the basis of a probe into the origin, development, basic thoughts and characteristics of Case-Based Reasoning (CBR), the dissertation does research on how to apply CBR into each stage of the decision process, including the presentation, establishment and index, as well as the revision and learning, and maintenance of the decision case-base. It also discusses the application of k-NN technique in CBR.The paper presents a new Optimized Case-based Reasoning--Intelligent Decision Technique (OCBR-IDT), and applies it to construct an intelligent decision support system. It adapts the man-machine coordinate techniques, integrates the man's brainpower and machine intelligence, combining the case-based reasoning with rule-based reasoning. This improves the flexibility,adaptation of the system and its assistance degree to decision process.2. On review of the strategies of the attributes selection, its process is formalized. The thesis investigates two strategies of attributes selection based on entropy, e.g. information gain and gain ratio. And its performance has be examined by using the strata k—fold cross validation and the k-NN techniques.Employing the genetic operator, which characterizes genetic algorithm, as the searching approach and correlation-based heuristic as the evaluating mechanism, the thesis presents a GA-CFS method to select the optimal subset of attributes for a given case library. Then the author combines the C4.5 algorithm with k-fold cross validation to evaluate its classification performance. The compared experimental results indicate that the proposed method can identify the most related subset for classification and prediction, while mostly reducing the representation space of the attributes whereas hardly decreasing the classification precision.Attributes selection based on the Prime Component Analysis (PCA) is considered, from total PCA to standard variance PCA, and algorithms of sample PAC are given. Using Iris data as test case base, the selected Prime Components' classification performance is checked by C4.5 and k—NN method. The experimental result shows that it can depress the data dimension as well as achieve certain classification precision.3. On analysis of the types, tasks, heuristics and similarity measure of case retrieval, the dissertation considers the measure of the case similarity from various dimensions, namely, between cases and between attributes of two cases. The methods of similarity measure based on the geometry model and the properties attributes are also analyzed.After introducing the methods of the commission problem-solving and syntactic weight sum, a multi-strategies similar case retrieval technique is presented, which is based on the boosting and vote. It can colligate multi model, collect group intelligence, overcome the lack of credibility in single model decision, and decrease the decision risk, while increase the robust and intelligence of decision process.4. The critical parameters in Tabu Search (TS) algorithm designs are discussed in detail, i.e. neighbors and neighbor search, tabu list and size of the tabu list, explicit memory and attributive memory, stop criterion and search efficiency. The author also explores the setting means of these parameters, its realization and influence on algorithm.The TSP problem belonging to map structure case is investigated. First the author describes the TSP problem. Then the author solves the 20 city TSP problem randomly generated using the na(?)ve TS algorithm and tests the convergence of the algorithm and optimization process of the case solution. Finally, the author investigates the case solution, which is based on the heuristics and reaches to the result of 20 city TSP problems using initial key, which are generated by "greedy" search strategy based on the heuristics TS algorithm.The case retrieval technique of map structure using the TS algorithm is presented. First the definition of map structure case and the similarity measure of it are put forward. Then its realization using TS algorithm and a two-stage case retrieval method is presented. It uses the naive and advance TS algorithm as well. Finally, the author investigates the improving strategies of TS.5. The author presents an overview of intelligent diagnosis systems' applications in medicine in domestic and abroad, especially in Traditional Chinese Medicine (TCM), analyses its important research value and significance using case-based reasoning technique into TCM. The OCBR—IDT technique is applied into the Xin'an medicine belonging to Hui School which is one of the three prominent schools in China, and constructs an intelligent stroke prevention and treatment system based on Xin'an medicine, from the concept design, structure design to its development. It can provide the disease diagnosis, distinguishing evidence and determining treatment, recipes and herb with intelligent decision support. It contributes much to the modernization, information and intelligence of Xin'an TCM.