The Research On Knowledge Reduction Algorithm Based On Rough Set And Its Application

The study on how to automatically extract valuable information and knowledge from large scale of data has become very active research area in current artificial intelligence research. Nowadays, knowledge discovery is facing the problems that incomplete and uncertain data is not processed effectively and interpretability of knowledge is weak. As a new soft computing method, rough set theory is the extension of set theory, and it is efficient in processing incomplete and uncertain data without knowing prior knowledge and external parameters. And it has been successfully used in areas of artificial intelligence, data mining, pattern recognition, and so on. Therefore, the research of knowledge discovery technology based on rough set theory is of great practical significance.In this dissertation, basic theories and conceptions of rough set are analyzed and studied. And in the framework of them, these researches are done:(1) Discretization of continuous attributes in rough setDiscretization of continuous attributes in rough set requires: the indiscernibility of decision system can not be changed by results of discretization so as to make sure that classification capacity of the decision system is not going to change; and the number of breakpoints in breakpoints set is as small as possible. Aiming at these two requests, firstly some discretization algorithms of continuous attributes are introduced, and they are studied and analyzed to expose their deficiencies on the above aspects or other ones; after that, aiming at these deficiencies, a mew data discretization algorithm based on advanced genetic algorithm is proposed; at last, experiments are carried out to prove its performance.(2) Attribute reduction in rough setAiming at the problem of inefficiency and low velocity with the traditional attribute reduction algorithm, an attribute reduction algorithm based on conditional information entropy and correlation coefficient is proposed, which changes attribute reduction process of non core attributes in the decision table into calculation of correlation coefficient, and reduces the number of scanning decision table, algorithmic time complexity and redundancy of the algorithm, and improves the efficiency of attribute reduction. Then the k-fold rotation comparison method is used to calculate correlation coefficient, which largely reduces calculation amount, and attains sub optimal attribute reduction result. The algorithm details are given and an experiment is carried out, the result of which verifies the efficiency of the algorithm.(3) Intelligent fault diagnosis system of fuel injection system in diesel enginesbased on rough set theoryRelative researches on rough set theory in this dissertation are used in fault diagnosis. After the introduction of diesel engine and its fuel injection system, theoretic basis of fault diagnosis based on rough set theory is analyzed, and an intelligent fault diagnosis system of fuel injection system in diesel engines based on rough set theory is established to help the staff finish fault diagnosis job better.