Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ...Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.展开更多
A design method based on the tree-model structure for topology update is presented. The routing tree of every node in network is built by defining the data structure and is used to save the topology information of nei...A design method based on the tree-model structure for topology update is presented. The routing tree of every node in network is built by defining the data structure and is used to save the topology information of neighbor nodes. The node topology update is accomplished by exchanging their routing trees. For saving the precious wireless bandwidth, the routing tree is sparsely shaped before sending by pruning the redundant routing information. Then, the node topology update is implemented by using algorithms of inserting and deleting routing sub-trees.展开更多
基金supported by the Science and Technology Plan Projects of Sichuan Province of China under Grant No.2008GZ0003the Key Technologies R & D Program of Sichuan Province of China under Grant No.2008SZ0100
文摘Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
文摘A design method based on the tree-model structure for topology update is presented. The routing tree of every node in network is built by defining the data structure and is used to save the topology information of neighbor nodes. The node topology update is accomplished by exchanging their routing trees. For saving the precious wireless bandwidth, the routing tree is sparsely shaped before sending by pruning the redundant routing information. Then, the node topology update is implemented by using algorithms of inserting and deleting routing sub-trees.
