Research of Individual Neural Network Generation and Ensemble Algorithm Based on Quotient Space Granularity Clustering

The aim of this paper is to develop an individual neural network generation and ensemble algorithm based on quotient space granularity clustering. Firstly, we give the characteristics of the quotient space granularity and affinity propagation(AP) clustering. Secondly, we introduce the quotient space concept to the AP clustering analysis, which can find an optimal granularity from all possible granularities. Then using improved AP clustering algorithm to seek optimal results of sample clustering and using different individual neural network to learn different categories of samples so that the degree of difference between networks and the generalization ability of neural network ensemble(NNE) can be improved. Further, according to the degree of correlation between the input data and the sample category to adaptively adjust ensemble weights. The algorithm proposed here is not only a method of generating the individual neural networks, but also can adaptively adjust ensemble weights of individual neural network. Experiments show that our proposed method is validity and correctness

Research and Development of Granular Neural Networks

Granular neural networks(GNNs) as a new calculation system structure based on Granular Computing(GrC) and artificial neural network can be able to deal with all kinds of granular information of the real world. This article has made the summary on the development and the present situation of GNNs. Firstly, it introduces the basic model of GrC: word calculation model based on fuzzy sets theory, rough sets model, granular computing model based on quotient space theory and so on, summarizes the research progress of fuzzy neural networks(FNNs) and rough neural networks(RNNs), then analyses the ensemble-based methods of GNNs, researches their meeting point of three main GrC methods, and finally points out the research and development direction of GNNs

a fuzzy support vector machine algorithm with dual membership based on hypersphere

In traditional fuzzy support vector machine(FSVM), membership function is established in global scope will reduce the membership of support vectors, and the FSVM based dismissing margin increases the training speed, but will remove some support vector artificially. So, a new algorithm of Fuzzy Support Vector Machine with Dual Membership based on Hypersphere (HDM-FSVM) is proposed. In this algorithm, the two classes of hyperspheres are divided into two parts respectively. Then, according to most support vectors are in the hemispheres which close together, we use the membership function that can enhance the membership of support vector, and because of there are a few of support vectors in other hemispheres, we must ensure the high membership of support vectors and reduce the membership of non-support vector. In order to removal noise and outliers, we introduce a radius controlling factor to control size of hyperspheres, the samples that outside of hyperspheres are considered as noise and outliers. Experimental results show that HDM-FSVM can enhance the classification accuracy rate of the sample sets that contain noise and outliers. Copyright © 2011 Binary Information Press.In traditional fuzzy support vector machine(FSVM), membership function is established in global scope will reduce the membership of support vectors, and the FSVM based dismissing margin increases the training speed, but will remove some support vector artificially. So, a new algorithm of Fuzzy Support Vector Machine with Dual Membership based on Hypersphere (HDM-FSVM) is proposed. In this algorithm, the two classes of hyperspheres are divided into two parts respectively. Then, according to most support vectors are in the hemispheres which close together, we use the membership function that can enhance the membership of support vector, and because of there are a few of support vectors in other hemispheres, we must ensure the high membership of support vectors and reduce the membership of non-support vector. In order to removal noise and outliers, we introduce a radius controlling factor to control size of hyperspheres, the samples that outside of hyperspheres are considered as noise and outliers. Experimental results show that HDM-FSVM can enhance the classification accuracy rate of the sample sets that contain noise and outliers. Copyright © 2011 Binary Information Press

Mixed and Continuous Strategy Monitor-Forward Game Based Selective Forwarding Solution in WSN

Wireless sensor networks are often deployed in unattended and hostile environments. Due to the resource limitations and multihop communication in WSN, selective forwarding attacks launched by compromised insider nodes are a serious threat. A trust-based scheme for identifying and isolating malicious nodes is proposed and a mixed strategy and a continuous strategy Monitor-Forward game between the sender node and its one-hop neighboring node is constructed to mitigate the selective dropping attacks in WSN. The continuous game will mitigate false positives on packet dropping detection on unreliable wireless communication channel. Simulation results demonstrate that continuous Monitor-Forward game based selective forwarding solution is an efficient approach to identifying the selective forwarding attacks in WSN

No-Reference Image Quality Assessment through SIFT Intensity

SIFT (Scale Invariant Feature Transform) points are scale-space extreme points, representing local minutiae structure features in the Gaussian scale space. SIFT intensity, as a novel no-reference metric, is feasible to assess various common distortions without the access to reference images. The metric introduces image preprocessing: neighborhood enhancement through contrast enhancement of adjacent pixels to reduce false SIFT points triggered by random signals; double-size image magnification through linear interpolation to amplify distortion effects to improve its sensitivity to image quality. SIFT intensity is defined as the number of SIFT points in a unit region and is calculated based on the first octave of the difference-of-Gaussian scale space. Experimental results demonstrate that SIFT intensity is superior to existing classic no-reference metrics and can be used to assess different distortions

Supervised feature extraction algorithm based on improved polynomial entropy

Abstract. Feature extraction plays an important part in pattern recognition (PR), data mining, machine learning et al. In this paper, a novel supervised feature extraction algorithm based on continuous divergence criterion (CDC) is set up. Firstly, the concept of the CDC is given, and some properties of the CDC are studied, and proved that CDC here is a kind of distance measure, i.e. it satisfies three requests of distance axiomatization, which can be used to measure the difference degree of a two-class problem. Secondly, based on CDC, the basic principle of supervised feature extraction are studied, a new concept of accumulated information rate (AIR) is given, which can be used to measure the degree of feature compression for two-class, and a new supervised feature extraction algorithm is constructed. At last, the experimental results demonstrate that the algorithm here is valid and reliable