1 |
曲卫华, 颜志军. 环境污染、经济增长与医疗卫生服务对公共健康的影响分析——基于中国省际面板数据的研究[J]. 中国管理科学, 2015,23(7): 166-176.
|
|
Qu Weihua, Yan Zhijun. The influence of environmental pollution, economic growth and healthcare services to public health based on China’s provincial panel data[J]. Chinese Journal of Management Science,2015,23(7):166-176.
|
2 |
Ferlay J, Colombet M, Soerjomataram I, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and method[J]. International Journal of Cancer, 2019, 144(8):1941-1953.
|
3 |
Park K, Ali A, Kim D, et al. Robust predictive model for evaluating breast cancer survivability[J]. Engineering Applications of Artificial Intelligence, 2013, 26(9): 2194-2205.
|
4 |
曾照芳, 安琳, 王济明. Cox回归模型在肝癌病人生存分析中应用的研究[J]. 生物数学学报, 2004, 19(1): 93-97.
|
|
Zeng Zhaofang, An Lin, Wang Jiming. The application of Cox regression model in the survival analysis of liver cancer[J]. Journal of Biomathematics,2004,19(1):93-97.
|
5 |
梁而慷, 曹子昂, 郑家豪, 等. 全肺切除术后辅助化疗的5年生存分析[J]. 中国癌症杂志, 2013, 23(5): 375-381.
|
|
Liang Erkang, Cao Ziang, Zheng Jiahao, et al. An analysis of 5-year survival after pneumonectomy and adjuvant chemotherapy in lung cancer[J]. China Oncology,2013,23(5):375-381.
|
6 |
王宇燕, 王杜娟, 王延章, 等. 改进随机森林的集成分类方法预测结直肠癌存活性[J]. 管理科学, 2017, 30(1): 95-106.
|
|
Wang Yuyan, Wang Dujuan, Wang Yanzhang, et al. Predicting survivability of colorectal cancer by an ensemble classification method improved on random forest[J]. Journal of Management Science,2017,30(1):95-106.
|
7 |
Shukla N, Hagenbuchner M, Win K T, et al. Breast cancer data analysis for survivability studies and prediction[J]. Computer Methods and Programs in Biomedicine, 2018, 155: 199-208.
|
8 |
Tapak L, Shirmohammadi K N, Amini P, et al. Prediction of survival and metastasis in breast cancer patients using machine learning classifiers[J]. Clinical Epidemiology and Global Health, 2019, 7(3): 293-299.
|
9 |
Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods[J]. Artificial Intelligence in Medicine, 2005, 34(2): 113-127.
|
10 |
Chen Zhi, Duan Jiang, Yang Cheng, et al. SMLBoost-adopting a soft-margin like strategy in boosting[J]. Knowledge-Based Systems, 2020,195: 105705-105718.
|
11 |
雷雪梅, 谢依彤. 用于高血压菜谱识别的基于遗传算法的改进XGBoost模型[J]. 计算机科学, 2018, 45(6A): 476-481.
|
|
Lei Xuemei, Xie Yitong. Improved XGBoost model based on genetic algorithm for hypertension recipe recognition[J]. Computer Science,2018,45(6A):476-481.
|
12 |
张涛, 郝晓玲, 张玥杰. 基于BP-AsymBoost的医疗诊断模型[J]. 系统工程理论与实践, 2017, 37(6): 1654-1664.
|
|
Zhao Tao, Hao Xiaoling, Zhang Yuejie. A medical diagnosis model based on BP-AsymBoost algorithm[J]. System Engineering Theory and Practice,2017,37(6):1654-1664.
|
13 |
蒋艳霞, 徐程兴. 基于集成支持向量机的企业财务业绩分类模型研究[J]. 中国管理科学, 2009, 17(2): 42-51.
|
|
Jiang Yanxia, Xu Chengxing. Analysis of classification model of companies’ financial performance based on integrated support vector machine[J]. Chinese Journal of Management Science,2009,17(2):42-51.
|
14 |
Ke Guolin, Meng Qi, Finely T, et al. LightGBM :a highly efficient gradient boosting decision tree[C]//Proceedings of the 31st Conference on the Neural Information Processing Systems, Long Beach, California, USA, December 4-9, 2017.
|
15 |
谢勇, 项薇, 季孟忠, 等. 基于Xgboost和LightGBM算法预测住房月租金的应用分析[J]. 计算机应用与软件, 2019, 36(9):1-10.
|
|
Xie Yong, Xiang Wei, Ji Mengzhong, et al. An application and analysis of forecast housing rental based on XGBoost and LightGBM algorithms[J]. Computer applications and software,2019,36(9):1-10.
|
16 |
Ma Xiaojun, Sha Jinglan, Wang Dehua, et al. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning[J]. Electronic Commerce Research and Applications, 2018, 31: 24-39.
|
17 |
Sun Xiaolei, Liu Mingxi, Sima Z. A novel cryptocurrency price trend forecasting model based on LightGBM[J]. Finance Research Letters, 2020, 32: 101084.
|
18 |
张渊, 张聪, 李开源, 等. ICU患者急性肾损伤发生风险的LightGBM预测模型[J]. 解放军医学院学报, 2019, 40(3):316-320.
|
|
Zhang Yuan, Zhang Cong, Li Kaiyuan, et al. LightGBM model for predicting acute kidney injury risk in ICU patients[J]. Academic Journal of Chinese Pla Medical School,2019,40(3):316-320.
|
19 |
陈荣,梁昌勇,陆文星,等.面向旅游突发事件的客流量混合预测方法研究[J]. 中国管理科学, 2017, 25(5): 167-174.
|
|
Chen Rong, Liang Changyong, Lu Wenxing, et al. The research of tourist flow hybrid forecasting model for tourism emergency events[J]. Chinese Journal of Management Science,2017,25(5):167-174.
|
20 |
Cui Shaoze, Wang Dujuan, Wang Yanzhang, et al. An improved support vector machine-based diabetic readmission prediction[J]. Computer Methods and Programs in Biomedicine, 2018, 166: 123-135.
|
21 |
Zhu You, Zhou Li, Xie Chi, et al. Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach[J]. International Journal of Production Economics, 2019, 211: 22-33.
|
22 |
Wang Chenshuo, Chen Xianxiang, Du Lidong, et al. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease[J]. Computer Methods and Programs in Biomedicine, 2020, 188: 105267-105275.
|
23 |
郭海湘, 黄媛玥, 顾明赟, 等. 基于自适应多分类器系统的甲状腺疾病诊断方法研究[J]. 系统工程理论与实践, 2018, 38(8): 2123-2134.
|
|
Guo Haixiang, Huang Yuanyue, Gu Mingbin, et al. Thyroid disease diagnosis method research based on adaptive multiple classifier system[J]. System Engineering Theory and Practice, 2018, 38(8):2123-2134.
|
24 |
Ramirez G S, Mourino T H, Martinez R D, et al. An information theory-based feature selection framework for big data under apache spark[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(9): 1441-1453.
|
25 |
Kalousis A, Prados J, Hilario M. Stability of feature selection algorithms: a study on high-dimensional spaces[J]. Knowledge and Information Systems, 2007, 12(1): 95-116.
|
26 |
Meinshausen N, Buhlmann P. Stability selection [J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2010, 72(4): 417-473.
|
27 |
肖进, 刘潇潇, 谢玲, 等.代价敏感的目标客户选择半监督集成模型研究[J]. 中国管理科学, 2018, 26(11): 186-196.
|
|
Xiao Jin, Liu Xiaoxiao, Xie Ling, et al. A cost-sensitive semi-supervised ensemble model for customer targeting[J]. Chinese Journal of Management Science,2018,26(11):186-196.
|
28 |
钟震亚. 年轻人早期胃癌恶性度的探讨[J]. 黑龙江医药科学, 1979, 4(4): 85-86.
|
|
Zhong Zhenya. Exploration of the malignancy of early gastric cancer in young people[J]. Heilongjiang Medicine and Pharmacy,1979,4(4):85-86.
|
29 |
Kim H J, Chang W K, Mi K K, et al. Dietary factors and gastric cancer in Korea: a case‐control study[J]. International Journal of Cancer, 2002, 97(4):531-5.
|
30 |
Kim E J, Yoon S J, Jo M W, et al. Measuring the burden of chronic diseases in Korea in 2007[J]. Public Health, 2013, 127(9):806-813.
|