Abstract. In recent years, complex network models were developed to solve classification and time series prediction problems. In this paper, ensemble classifier based on complex network (mainly scale-free network) is firstly used to predict protein structural class. For the classifier design, genetic programming and particle swarm optimization algorithm are used alternately to evolve the structure and encoding parameters. The experimental results validate the good performance of the proposed method.

Abstract-Interactions among proteins are the basis of various life events. So, it is important to recognize and research protein interaction sites. A control set that contains 149 protein molecules were used here. Then 10 features were extracted and 4 sample sets that contained 9 sliding windows were made according to features. These 4 sample sets were calculated by Radial Basis Functional neutral networks which were optimized by Particle Swarm Optimization respectively. Then 4 groups of results were obtained. Finally, these 4 groups of results were integrated by decision fusion (DF) and Genetic Algorithm based Selected Ensemble (GASEN). A better accuracy was got by DF and GASEN. So, the integrated methods were proved to be effective.

The FlexibleNeural Tree uses a tree structure coding and has excellent predictiveability and function approximation capabilities. Due to it, a quantum neural tree model ispresented based on the multi-level transfer function quantum neuralnetwork and Flexible Neural Tree. In the new model, based on the structure of FlexibleNeural Tree, the transfer function of hidden layer quantum neurons is insteadof multiple superposition oftraditional transfer function, makes the model has a kind of inherent ambiguity.This paper used the improved neural tree asprediction model, particle swarm optimization to optimize the parameters of neuraltree, used probabilistic incremental program evolution to optimizethe structure of neural tree. The experiment result for stock index predictionshows the now method can improve the predictive accuracy rate

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

In this paper we intend to apply a new method to predict tertiary structure. Several feature extraction methods adopted are physicochemical composition, recurrence quantification analysis (RQA) , pseudo amino acid composition (PseAA) and Distance frequency. We construct the binary tree Classification model, and adopt flexible neural tree models as the classifiers. We will train a number of based classifiers through different features extraction methods for every node of binary tree, then employ the selective ensemble method to ensemble them. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 63.58%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.