Focusing on that the existing attribute reduction algorithms are not suitable for dealing with the incomplete data with both numerical attributes and symbolic attributes, an extented incomplete neighborhood rough set model was proposed. Firstly, the distance between the missing attribute values was defined to deal with incomplete data with mixed attributes by considering the probability distribution of the attribute values. Secondly, the concept of neighborhood mixed entropy was defined to evaluate the quality of attribute reduction and the relevant property theorem was proved. An attribute reduction algorithm for incomplete neighborhood rough set based on neighborhood mixed entropy was constructed. Finally, seven sets of data were selected from the UCI dataset for experimentation, and the algorithms was compared with the Attribute Reduction of Dependency (ARD), the Attribute Reduction of neighborhood Conditional Entropy (ARCE) and the Attribute Reduction of Neighborhood Combination Measure (ARNCM) algorithm respectively. The theoretical analysis and the experimental results show that compared to ARD, ARCE, ARNCM algorithms, the proposed algorithm reduces the attributes by about 1, 7, 0 respectively, and improves the classification accuracy by about 2.5 percentage points, 2.1 percentage points, 0.8 percentage points respectively. The proposed algorithm not only has less reducted attributes, but also has higher classification accuracy.

In order to make full use of the ability of of Synthetic Aperture Radar (SAR) images with different polarization features for characterizing different types of ground objects, an analysis and classification approach of polarimetric SAR feature based on multi-layer Support Vector Machine (SVM) classifier was proposed. Firstly, the optimal feature subsets suitable for different terrain types were determined through the feature analysis. Then, the method of hierarchical classification tree was used for SVM classification step by step according to the feature subset of each object type.Finally, the overall final result was obtained. The experimental results of RadarSAT-2 polarimetric SAR image classification show that, the classification accuracy of the proposed approach is approximately 85% for four kinds of ground objects such as water area, cultivated land, forest land and urban area and the overall classification accuracy is up to 86%. The proposed approach can make full use of the characteristics of the different ground object target types, improve the classification accuracy and reduce the time complexity.

Concerning the construction problem of attribute subset sequence in multi-granulation rough set model, a construction method based on the distance between attributes was proposed. Firstly, the concept of the distance between attributes in information system was introduced. Secondly, the quantitative calculation formula was given, which was then used to compute the distance between the attributes. Finally, according to the distance between the attributes, the neighborhood attribute set of each attribute was obtained, and then the attribute subset sequence was constructed. The experimental results show that the proposed method is more accurate for each object class of the experiment than the random constructional attribute subset sequence.

Facial feature change occur in varying degrees with the increase of age. These features involve the shape features and texture features, which increase the difficulty of face recognition. In order to accurately describe the facial features with change in age to improve the accuracy of face recognition, firstly, this paper applied the innovation of Gabor wavelet on the pyramid model to structure the Gabor pyramid characteristic sequence. This paper used mean grid to descend and denoise the Gabor pyramid characteristic sequence initially, and then reconstructed pyramid characteristic sequence of different samples at the same level and direction. Finally, this paper constructed forty parallel classifiers to classify by using Direct Fractional-step Linear Discriminant Analysis (DF-LDA) algorithm. The experimental results show that the Gabor pyramid characteristic sequence can improve the accuracy of face recognition with the change in age.

With serious sidelight, it is difficult for the traditional algorithm to eliminate shadows. To improve the illumination compensation effect, a logarithmic transformation function was presented. In order to improve the performance of face recognition, by taking this problem as a classic pattern classification problem, a new method combining Local Binary Pattern (LBP) and Support Vector Machine (SVM) was proposed. One-against-one was used to convert multi-class problem to two-class problem, that can be used by SVM. Simulation experiments were conducted on the database of CMU PIE, AR, CAS-PEAL and one face database collected by the authors. The results show that lighting effects can be well eliminated and the proposed method performs better than the traditional ones.

Rule based intrusion detection system is the mainstream of intrusion detection systems. For every incoming network packet, a scheme of creating a single rule set for pattern match was discussed. This scheme could reduce the work of pattern match, and improve the efficiency of system.