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Class overlapping is thought as one of the toughest problems in data mining because the complex structure of data. The current classification algorithms show little consideration of this problem. So when using this traditional classification algorithms to resolve this problem, classification performance is not good for samples in overlapping region. To meet this critical challenge, in this paper, we pay a systematic study on the class overlapping problem and propose a new classification algorithm based on NB for class overlapping problem (CANB). CANB uses NB to find class overlapping region and use this region and non-overlapping region in NB classification model learning separately. Experimental results on bench mark and real-world data sets demonstrate that CANB can improve the classification performances for class overlapping problem stably and effectively.

This paper studies the consensus problem of multiple agents with general linear continuous-time dynamics. It is assumed that the information transmission among agents is intermittent; namely, each agent can only obtain the information of other agents at some discrete times, where the discrete time intervals may not be equal. Some sufficient conditions for consensus in the cases of state feedback and static output feedback are established, and it is shown that if the controller gain and the upper bound of discrete time intervals satisfy certain linear matrix inequality, then consensus can be reached. Simulations are performed to validate the theoretical results.