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Ensemble techniques have been widely used for improving the classification accuracy, and recent studies show that ensembling classifiers through multi-modal perturbation can further improve the classification performance. In this paper, we propose a novel selective ensemble algorithm (called SE_AROS) based on approximate reducts and optimal sampling. In SE_AROS, we design a multi-modal perturbation...
Wireless sensor networks are often deployed to work in harsh or disaster and other special environments, such as earthquakes, floods, fires, other outer space and the battlefield. Owing to the lack of energy or disaster scenarios, sensor nodes may fail easily. This severe reduce the data persistence in the network and the efficiency of the sensed data acquisition. Growth Codes (GC) can work effectively...
In the field of reservoir numerical simulation, irregular iteration problem is one of the most important reasons to affect the efficiency of large-scale computing. Memory access patterns can't be determined at compiler-time, which brings us difficulties to parallel. This library uses Inspector/Executor model based on distributed clusters, and improves parallel efficiency utilizing alternate tiling...
According to the problems of the nonlinearity and non norm on dam displacement prediction, the dam displacement mode based on improved ant colony algorithm neural networks was proposed. The binary ant colony algorithm has been brought into the optimization of weights in neural networks. So that the shortcomings of the ant algorithm using in the combinatorial optimization in continuous field have been...
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