Time series classification (TSC) task is one of the most significant topics in data mining. Among all methods for this issue, the deep-learning-based shows superior performance for its good adaption to raw series data and automatic extraction of features. However, rare eyes are kept on composing ensembles of these superior individual classifiers to achieve further breakthroughs. The existing deep learning ensembles NNE did a heavy work of combining 60 individuals but did not maximize the deserving improvement, since it merely pays attention to the diversity of individuals but ignores their accuracy. In this paper, we propose to construct an ensemble of Full Convolutional Neural Networks (FCN) by Random Subspace Method (RSM), named RSM-FCN. FCN is a simple but outstanding individual classifier and RSM is suitable for high dimensional data such as time series, but there are few instances. Thus, the combination of these strengths, RSM-FCN provides a highly cost-effective approach to yield promising results. Experiments on the UCR dataset demonstrate the effectiveness and reasonability of the proposed method.
With the development of modern science and technology, information technology has brought great changes to many fields. Smart justice has become one of the increasing areas that people are paying more attention to. For example, large and small cases occur every day, and the legal library is continuously updated. Therefore, a large number of documents and evidence collection archives will bring tremendous pressure on the judiciary. The text generation technology can automatically present the results extracted from these redundant legal data and express the results of the analysis in natural language. It facilitates the business for huge amounts of legal data effectively, which relieves the work pressure of the judicial department. However, the text generation algorithms have not been promoted in justice. Therefore, this paper focuses on what benefits text generation can produce in law and how to apply text generation technology in legal field. The survey provides a comprehensive overview on text generation firstly, through summarizing the existing methods, that is, text to text, data to text, and visual to text. Then, we examine the process of the practical application of text generation in law. Furthermore, this paper puts forward the challenges and possible solutions to the judicial text generation, which provides pointers on future work.
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