Research On Support Vector Machine Based On Rough Set And Its Application

Machine learning based on data is an important issue to be addressed in modern intelligence technology, which also is an important method in data mining. In recent years, a lot of research focus on rough set (RS) and support vector machine (SVM) theory. SVM is based on structural rist minimization rule, which can solve some practical problems such as small samples, over learning, high dimensions and local minima, and has excellent generalization ability. However, there are still some problems with SVM, such as being sensitive to noises and too time-consuming. Some improvements from two different sides are proposed in this dissertation. On the one hand, attribute reduction based on rough set is used to preprocess the dataset for SVM. On the other hand, fuzzy theory and rough set are introduced to improve SVM in dealing with indeterminate problems.The contributions of this dissertation include:1. Support vector machine can not directly deal with high dimension and large scale training set and it is sensitive to abnormal samples, an improved support vector classifier based on neighborhood rough set is proposed. In the paper, data preprocessing is done on training set from two different sides. On the one hand, neighborhood rough set is used to find these samples in boundary and to obtain a reduced training set, at the same time, those abnormal samples which not only lead to over-learning but also decrease the generalization ability are deleted. On the other hand, attribute reduction is done and feature weight is imported based on attribute significance because different feature effects differently on classification.2. Attribute reduction based on rough set may get more than one reduced set of attributes, which describe the original system from different sides, there are diversity and complementarity between those reduced sets of attributes. A selective SVM ensemble classifier based on rough set attribute reduction is proposed。3. Rough one-class support vector machine based on rough set is researched and an improved method for determination membership of fuzzy support vector machine is proposed in the paper. Firstly, the smallest rough sphere which encloses the data points is constructed. Secondly, those data points are divided into upper approximation region, low approximation region and boundary region by the distance between data points and the center of the rough sphere. At last, three different ways are used to determine membership of each samples.4. Duo to the complexity of objects and limitation of cognition, some information systems including uncertain information are obtained, in these systems, not that each sample definitely belongs to one class, but that it belongs to one class by a probability. A kind of fuzzy equivalent relation is constructed by a kind of special kernel function, and then the definition of kernel fuzzy rough set is proposed based on fuzzy equivalent relation, the operator of low approximation in kernel fuzzy rough set is used to assign membership for each sample, and a new optimization problem is constructed with those samples.