Science Project

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Project number Project name Project origin Start time Roles Project kind
2018AAA0102201 恶劣环境下视觉信息的主动探测与感知 国家重点研发计划项目 2019-12~ 骨干成员 Vertical project
11671317 基于概率生成模型的高维数据变量选择 国家自然科学基金项目 2017-1~ 负责人 Vertical project
11201367 面向高维小样本数据的集成分类方法研究 国家自然科学基金项目 2013-1~ 负责人 Vertical project
11126277 聚类集成方法在往复式压缩机故障诊断中的应用研究 国家自然科学基金项目 2012-1~ 负责人 Vertical project
61075006 一类适用于K-最近邻分类的非负矩阵分解方法研究 国家自然科学基金项目 2011-1~ 骨干成员 Vertical project
20100201120048 集成学习中有关算法的研究 国家教育部项目 2011-1~ 负责人 Vertical project
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Showing 1 to 6 of 6 entries.

Research Fields

  1. Chun-Xia Zhang*, Shuang Xu, Jiang-She Zhang. A novel variational Bayesian method for variable selection in logistic regression models. Computational Statistics and Data Analysis, 2019, 133: 1-19.

  2. Chunxia Zhang*, Yilei Wu, Mu Zhu. Pruning variable selection ensembles. Statistical Analysis and Data Mining, 2019, 12(3): 168-184.

  3. Chun-Xia Zhang*, Sang-Woon Kim, Jiang-She Zhang. On selective learning in stochastic stepwise ensembles. International Journal of Machine Learning and Cybernetics, 2020, 11(1): 217-230.

  4. Shuang Xu, Chun-Xia Zhang*. Robust sparse regression by modeling noise as a mixture of gaussians. Journal of Applied Statistics, 2019, 46(10): 1738-1755.

  5. Bao Guo, Chunxia Zhang, Junmin Liu*, Xiaoyi Ma. Improving text classification with weighted word embedding via a multi-channel TextCNN model. Neurocomputing, 2019, 363: 366-374.

  6. Wang Guan-wei, Zhang Chun-xia*, Yin Qing-yan. RS-BART: a novel technique to boost the prediction ability of Bayesian additive regression trees. Chinese Journal of Engineering Mathematics, 2019, 36(4): 461-477.

  7. 张春霞, 李俊丽. 变量选择集成方法. 工程数学学报, 2019, 36(1): 1-17.

  8. 张春霞, 王姣姣, 李国兵. 基于图正则化自编码的深度极端学习机模型. 计算机应用研究, 2019, 36(增刊): 148-150.

  9. Chun-Xia Zhang*, Jiang-She Zhang, Qing-Yan Yin. Early stopping aggregation in selective variable selection ensembles for high-dimensional linear regression models. Knowledge-Based Systems, 2018, 153: 1-11.

  10. Chun-Xia Zhang*, Jiang-She Zhang, Guan-Wei Wang, Nan-Nan Ji. A novel bagging approach for variable ranking and selection via a mixed importance measure. Journal of Applied Statistics, 2018, 45(10): 1734-1755.

  11. Chun-Xia Zhang*, Jiang-She Zhang, Qing-Yan Yin. A ranking-based strategy to prune variable selection ensembles. Knowledge-Based Systems, 2017, 125: 13-25.

  12. Chun-Xia Zhang*, Jiang-She Zhang, Sang-Woon Kim. PBoostGA: pseudo-boosting genetic algorithm for variable ranking and selection. Computational Statistics, 2016, 31(4): 1237-1262.

  13. Chun-Xia Zhang*, Nan-Nan Ji, Guan-Wei Wang. Randomizing outputs to increase variable selection accuracy. Neurocomputing, 2016, 218: 91-102.

  14. Chun-Xia Zhang*, Guan-Wei Wang, Jun-Min Liu. RandGA: injecting randomness into parallel genetic algorithm for variable selection. Journal of Applied Statistics, 2015, 42(3): 630-647.

  15. Chun-Xia Zhang*, Jiang-She Zhang, Guan-Wei Wang. A novel bagging ensemble approach for variable ranking and selection for linear regression models. In: Proceedings of the 12th International Workshop on Multiple Classifier Systems, 2015, Günzburg, Germany, LNCS, vol. 9132, pp. 3-14.

  16. 张春霞*, 姬楠楠, 王冠伟. 受限波尔兹曼机. 工程数学学报, 2015, 32(2): 159-173.

  17. Chun-Xia Zhang, Guan-Wei Wang*, Jiang-She Zhang, Gao Guo, Qing-Yan Yin. IRUSRT: a novel imbalanced learning technique by combining inverse random under sampling and random tree. Communications in Statistics-Simulation and Computation, 2014, 43(10): 2714-2731.

  18. Chun-Xia Zhang*, Jiang-She Zhang, Nan-Nan Ji, Gao Guo. Learning ensemble classifiers via restricted Boltzmann machines, Pattern Recognition Letters, 2014, 36: 161-170.

  19. Chun-Xia Zhang*, Guan-Wei Wang. Boosting variable selection algorithm for linear regression models. In Proceedings of the 10th International Conference on Natural Computation, Xiamen, China, 2014, pp. 769-774.