Hybrid Case Based Reasoning System And Application

Case-based reasoning is a machine learning algorithm based on human cognitive models.It efficiently solves similar new problems based on previous cases or experiences and has been widely used in various fields.However,the traditional case-based reasoning system has limitations such as weight imbalance and excessive reliance on expert experience in assigning feature weights,and the efficiency of case-based reasoning has been limited as the size of the case database continues to grow.Therefore,in this paper,aiming at the limitations of traditional case reasoning,this paper conducts research from two aspects of feature weighting and case organization to build a hybrid case reasoning system.At the same time,the hybrid case-based reasoning system is applied to the generation of public security emergency plans,and an intelligent public security emergency plan system is designed and implemented.Its main innovations and work are as follows.First,a random forest weighted KNN(k-Nearest Neighbor,K nearest neighbor)algorithm is proposed.Based on the analysis of the basic principles and case structure of case-based reasoning,in view of the shortcomings of feature weighting in traditional case-based reasoning systems,a random forest algorithm was introduced in the case-retrieval stage of case-based reasoning to provide appropriate feature weights for the similarity measurement algorithm for case retrieval..The performance of the proposed algorithm is tested through experiments,and the experimental results prove that the proposed algorithm has higher accuracy.Secondly,an improved growing hierarchical self-organizing mapping algorithm is used to optimize the case inference system.Because the traditional case inference system needs to measure the similarity between the new case and all cases in the case database during case retrieval,the accuracy and efficiency of case retrieval is low.Therefore,an improved growing hierarchical self-organizing mapping algorithm(GHSOM)is used for case organization,and the initial case database is divided into several sub-case databases according to the similarity.When the case is retrieved,the corresponding sub-case database is located first,and then the sub-cases are retrieved.The cases in the casebase that are most similar to the new problem have been verified through experimental tests to improve the accuracy and efficiency of case retrieval.Finally,an intelligent public security emergency response plan system was designed and implemented.In view of the current public security emergency response plan,such as a single form,a low degree of digitization,and inability to be put into use in a timely manner.Based on the analysis of the basic concepts and requirements of the public security emergency plan,the proposed hybrid case based reasoning system is applied to the automatic generation of public security emergency plans,Eclipse is used as the compilation platform,and PyQt5 is used as the user graphical interface development framework to design and implement intelligent public security emergency response plan system.The system includes five functional modules: user module,case management module,plan management module,case database module,and case based reasoning module,which helps public security organs deal with public emergencies more efficiently.