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机器智能研究（英文）
机器智能研究（英文）

谭铁牛　刘国平　胡豁生

双月刊

2731-538X

ijac@ia.ac.cn

010-62655893

100190

北京海淀区中关村东路95号2728信箱

机器智能研究（英文）/Journal Machine Intelligence ResearchCSCDCSTPCD北大核心EI
查看更多>>International Journal of Automation and computing is a publication of Institute of Automation, the Chinese Academy of Sciencs and Chinese Automation and computing Society in the United Kingdom. The Journal publishes papers on original theoretical and experimental research and development in automation and computing. The scope of the journal is extensive. Topics include; artificial intelligence, automatic control, bioinformatics, computer sciene, information technology, modeling and simulation, networks and communications, optimization and decision, pattern recognition, robotics, signal processing, and systems engineering.
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    Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

    Hui GuanGuorong CaiHang Xu
    993-1010页
    查看更多>>摘要：Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automat-ic requirement dependency extraction method based on integrated active learning strategies.In this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence degree.By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples.As the optimal samples are continuously added into the initial training set,the per-formance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved.Therefore,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples.Finally,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71％,the weight-recall by 2.45％,and the weight-precision by 2.64％ in comparison with other strategies,saving approx-imately 46％ of the labelled data compared with the machine learning approach.
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