The Research On Application Of Improved ID3 Of Decision Tree Classification Algorithm In Management Of Students' Grades

By the end of last century, Data Mining (DM) has become a kind of significant intelligent data analysis technology, with which the helpful knowledge of pattern will be drawn out or discovered automatically from vast various types of data which saved in databases or data warehouses. In this process, classification of data is the important topic in the research field of data mining.Decision tree method has been widely researched and applied for its systematic clearness of the theory and its wide availability for researchers. What's more, it is easily transformed into"IF-THEN"rules of classification.In this dissertation, based on the teaching platform of embedded online intelligence, the research on how to apply the DM echnology to employed EDC system to distill the helpful information which is hidden inside the mass data in order to offer a tool of synthetic analysis, decision-making assistant and support for administrator and decision-maker is discussed. According to the research result of the basic algorithm of the decision-making tree, we develop one module of the evaluation system of student grades, which is based on student achievement evaluation tool derived from improved ID3 algorithm. From the data of student marks, personal information and evaluation information, establishes a decision tree performance appraisal model, carries on the corresponding generalized analysis appraisal to the student.Through carries on the analysis comparison to several kinds of typical decision tree algorithms, this article proposes one kind of improvement ID3 algorithm. This cure thought of the improved algorithm is combined the Taylor formula principle with the solution of information entropy which is the standard of attribute selection. Using mathematics Taylor formula and giving a power value N to information entropy for all attributes, the value N is determine to the number of attribute value. The new algorithm will reduce complication of calculate and enhance the efficiency. Moreover, it can make a more reasonable choice of splitting attribute.At last,ID3 algorithm and the new algorithm based on ID3 are developed on the eclipse platform by Java.Some aetual training dataset are adopt to test and the experimental results show that the new algorithm can raise the process of making decision tree and reduce comPliation of time,at the same time ,it can overcome the ID3's weakness which is apt to select some attribute with more value .Furthermore,the performance of classification tree becomes better and with the enlarging of dataset scale.Both the theoretical analysis and the experimental comparion show that the algorithm proposed in this thesis has better improved performance than ID3 algorithm and expresses a good result for classification.