Study on Knowledge Reduction Based on Rough Set and Its Application in Continuing Education
|Course||Computer Science and Technology|
|Keywords||Rough Set Incomplete Information System Rough Entropy Knowledge Reduction Teacher Continuing Education Evaluation System|
Today, society has already entered the era of network information, primary and secondary school teachers to continue education and training from the traditional face-to-face mode into the modern distance education mode. In this mode, the trainees learning content, learning methods, time, place of study have undergone profound changes. How comprehensive and accurate way to assess the quality of teachers to participate in continuing education and training to become an important research topic. Now, teacher training institutions in the study of teachers' continuing education evaluation system. These evaluation system by giving the evaluation, collecting evaluation data through data mining, and then evaluate decision-making. However, due to the processing of data from the network, these evaluation systems almost always faced with the problem of large amount of data, the data is not complete. Proposed in 1982 by the Z.Pawlak rough set theory happens to be able to solve the above problems. Rough set theory is a deal with uncertain and incomplete knowledge of mathematical tools. The theory above On domain indiscernibility relation, and use, the next approximation to describe the concept, does not require any additional information or prior knowledge, will be able to effectively analyze and deal with imprecise, incomplete and inconsistent data . In this paper, teachers' continuing education evaluation system for the actual background based on the traditional rough set theory, research knowledge reduction algorithm in incomplete information systems. Firstly, rough set theory, analysis of the existing extension of rough set model limitations and lack of tolerance relation, an improved tolerance relation instance proved, the model with Yung poor relations compared to the stronger classification ability, more in line with the objective reality, and increase flexibility. Then introduce a tolerance relation-based rough entropy and the knowledge reduction algorithm analysis tolerance relation-based rough entropy deficiencies, the definition of the rough entropy based on improved tolerance relation. Then, to a new deal with incomplete information based on improved tolerance relation rough entropy attribute reduction algorithm. By an example of the effectiveness of the algorithm. The paper also describes a multi-valued distinguished matrix proposed reduction algorithm based on multi-valued distinguished matrix, and then apply it to get the decision-making rules. Finally, the paper design and elaboration of the overall structure and function of the teachers' continuing education assessment system, and data acquisition modules, trainees comprehensive assessment module detailed design, then the knowledge reduction algorithm is applied to paper evaluation system education assessment system based on rough set theory teachers continue data mining models.