周绮凤副教授 研究方向：机器学习与模式识别 数据挖掘及应用 故障诊断 图像处理
系别：
联系电话：
电子邮箱：zhouqf@xmu.edu.cn



个人简介
研究成果
承担项目
学术奖励


待补充


周绮凤，杨帆，李涛. 数据挖掘在建筑业中的应用，数据挖掘的应用与实践——大数据时代的案例分析[M]，厦门大学出版社，2013, pp. 332–378.
李涛、王鹏、周绮凤（著），数据挖掘的数学基础. 厦门大学出版社（ISBN：978-7-5615-4290-3/T.204）, 2015.
Qifeng Zhou, Hao Zhou, Tao Li, Cost-sensitive Feature Selection using Random Forest: Selecting Low-Cost Subsets of Informative Features. Knowledge-Based Systems, to appear (SCI, JCR 2).
Qifeng Zhou*, Hao Zhou, Qingqing Zhou, et.al., Structural damage detection based on posteriori probability support vector machine and Dempster–Shafer evidence theory. Applied Soft Computing, 36:368-374, 2015(SCI, JCR 2).
Qifeng Zhou, Hao Zhou, Yongpeng Ning, et.al., Two Approaches for Novelty Detection using Random Forest. Expert Systems With Applications, 42(10):4840-4850, 2015(SCI, JCR 3).
Qifeng Zhou, Bin Xia, Yexi Jiang, Qianmu Li, et.al., A Classification-Based Demand Trend Prediction Model in Cloud Computing. in Proceedings of 16 International Conference on Web Information System Engineering, Miami, USA, November, 2015(CCF C类会议).
Qifeng Zhou*, Hao Zhou, Yimin Zhu, Tao Li. Data-driven Solutions for Building Environmental Impact Assessment, in Proceedings of 9th IEEE International Conference on Semantic Computing , California, USA, February 2015.
李涛,周绮凤,郑理,黄越, 大数据提升灾难信息管理水平,大数据, 1(3):**—6.
周绮凤, 李涛, 大数据与计算可持续性, 南京邮电大学学报, 35(5):20-31.
Qifeng Zhou*, Hao Zhou, Yongpeng Ning, et.al., Structure Damage Detection Based on Random Forest Recursive Feature Elimination. Mechanical Systems and Signal Processing. 46(1):82-90, 2014(SCI, JCR 3).
Qifeng Zhou*, Jianchao Ding, Yongpeng Ning, et.al., Stable Feature Selection with Ensembles of Multi-reliefF, in Proceedings of 10th IEEE International Conference on Natural Computation, Xiamen, China, August 2014.
Qifeng Zhou*, Yongpeng ning, Qingqing Zhou, Jiayan Lei, Structural Damage Detection Method Based on Random Forests and Data Fusion. Structural Health Monitoring, 12(1):48-58, 2013 (SCI, JCR 3)


基于数据驱动的建筑环境影响评价关键问题研究，国家自然科学基金青年项目，2016-2018
稳健特征提取理论及关键技术研究，重点实验室开放课题，2015-2016