A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label
文献类型：会议论文
| 作者 | Luo, Mengzhen1; Shi, Xiaoyu2 ; Ji, Qianqian2; Shang, Mingsheng2; He, Xianbo1; Tao, Weiguo1,2
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| 出版日期 | 2020 |
| 会议日期 | July 20, 2019 - July 22, 2019 |
| 会议地点 | Kunming, China |
| DOI | 10.1007/978-3-030-32591-6_61 |
| 页码 | 566-573 |
| 英文摘要 | The classification of medical patents play an important role for pharmaceutical company, since medical patens with well labeled can significantly accelerate the process of new drug research. The previous studies using machine learning methods focus on classification the medical patents with single label. However, the classification of medical patents is a multi-label task, and the available data are always incomplete with losing the part information of patents. In this paper, we propose a deep self-learning classification framework that can deal with the incomplete medical patterns with multi-label issue. It consists of a text processor and patent classifier. For the text processor, a professional medical text thesaurus is built via GloVe method, which can learn more specialized vocabulary. For the patent classifier, we adopt a bidirectional long short term memory (Bi-LSTM) model to construct our patent classifier, which can learn the hidden knowledge from medical patents and associate with appropriate labels to medical patents automatically. Furthermore, an advanced focal loss function is design to further improve the classification accuracy. Experiments on the Thomson Reuters dataset demonstrate that our proposed method outperform the other existing methods in terms of precision and recall, when dealing with incomplete medical patents with multi-label issue.. © 2020, Springer Nature Switzerland AG. |
| 会议录 | 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019
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| 语种 | 英语 |
| 电子版国际标准刊号 | 21945365 |
| ISSN号 | 21945357 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9777]  |
| 专题 | 大数据挖掘及应用中心 |
| 作者单位 | 1.The Computer School of China West Normal University, Nanchong; Sichuan; 637002, China; 2.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China |
| 推荐引用方式 GB/T 7714 | Luo, Mengzhen,Shi, Xiaoyu,Ji, Qianqian,et al. A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label[C]. 见:. Kunming, China. July 20, 2019 - July 22, 2019. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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