The classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, there are significantly fewer training instances of one class compared to another class. Hence, the minority class instances are much more likely to be misclassified. In the literature, the synthetic minority over-sampling technique (SMOTE) has been developed to deal with the classification of imbalanced datasets. It synthesizes new samples of the minority class to balance the dataset, by re-sampling the instances of the minority class. Nevertheless, the existing algorithms-based SMOTE uses the same sampling rate for all instances of the minority class. This results in sub-optimal performance. To address this issue, we propose a novel genetic algorithm-based SMOTE (GAS-MOTE) algorithm. The GASMOTE algorithm uses different sampling rates for different minority class instances and finds the combination of optimal sampling rates. The experimental results on ten typical imbalance datasets show that, compared with SMOTE algorithm, GASMOTE can increase 5.9 % on F-measure value and 1.6 % on G-mean value, and compared with Borderline-SMOTE algorithm, GASMOTE can increase 3.7 % on F-measure value and 2.3 % on G-mean value. GASMOTE can be used as a new over-sampling technique to deal with imbalance dataset classification problem. We have particularly applied the GASMOTE algorithm to a practical engineering application: prediction of rockburst in the VCR rockburst datasets. The experiment results indicate that the GASMOTE algorithm can accurately predict the rockburst occurrence and hence provides guidance to the design and construction of safe deep mining engineering structures.

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There are several methods to forecast precipitation, but none of them is accurate enough since predicting precipitation is very complicated and influenced by many factors. Data assimilation systems (DAS) aim to increase the prediction result by processing data from different sources in a general way, such as a weighted average, but have not been used for precipitation prediction until now. A DAS that makes use of mathematical tools is complex and hard to carry out. In our paper, machine learning techniques are introduced into a precipitation data assimilation system. After summarizing the theoretical construction of this method, we take some practical weather forecasting experiments and the results show that the new system is effective and promising.

Abstract.Recently, more and more authors have been encouraged for collaboration because it often produces good results. However, the author collaboration network contains experts in various research directions within various fields, and it is difficult for individual authors to decide which authors are best suited to their expertise. This paper

Mining newsworthy events from a large number of microblogging information is not only the primary problem that several big microblogging websites need to solve, but also a new research field in micro-information age. For now, a lot of study about even recognizing has been made at home and abroad, but relatively rarely contrapose short text (microblogging message). The paper considers newsworthy event recognizing in short text as classification problem, utilizes the decision tree classification algorithm in data mining, sufficiently mines features of event in short text, and then recognizes the newsworthy event in microblogging. In the last, we verify the effect of the model.