简介：

王冉，深圳大学数学与统计学院助理教授。

联系方式：

邮箱： wangran@szu.edu.cn

电话：26538953

地址：科技楼312

研究领域：

机器学习、模式识别、大数据分析

具体方向：

• 监督学习与半监督学习

• 多分类问题与主动学习

• 模糊系统及粗糙集理论

• 计算智能与演化计算

• 大数据及时间空间数据分析

获得荣誉：

• 深圳市海外高层次人才“孔雀计划”C类人才

• 深圳大学2019年“荔园优青”培养对象

教学课程：

《模式识别》、《统计学习方法》、《算法设计与分析》、《操作系统》

科研项目：

• 01/2018–12/2021 多标记问题的不确定性分析与主动学习方法研究 (主持, 中国国家自然科学基金，面上项目61772344，62万)

• 04/2018–03/2020 多标记主动学习的关键问题：多目标优化、不确定性建模与多准则决策 (主持, 中国国家自然科学基金，国际合作与交流项目61811530324，10万)

• 01/2015–12/2017 基于分治融合与主动学习的极速学习机方法研究 (主持, 中国国家自然科学基金，青年基金项目61402460，24万)

• 01/2018–12/2022 面向大数据机器学习的不确定性建模理论与方法 (参与, 中国国家自然科学基金，重点项目61732011，285万)

• 01/2018–12/2020 大数据下多实例与多标记机器学习算法与应用 (主持, 深圳市高端人才科研启动项目，827-000230，270万)

• 06/2017–05/2019 多标记主动学习的理论建模与算法研究 (主持, 深圳大学青年教师科研启动项目2017060，6万)

期刊论文：

• Jingchao Cao, Shiqi Wang, Ran Wang, Xinfeng Zhang, and Sam Kwong*. Content-oriented image quality assessment with multi-label SVM classifier. Signal Processing: Image Communication, 78:388—397 (2019). (中科院大类3区)

• Yuheng Jia, Sam Kwong*, and Ran Wang. Applying Exponential Family Distribution to Generalized Extreme Learning Machine. IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, DOI: 10.1109/TSMC.2017.2788005 (2019). (中科院大类2区,小类1区top)

• Dasen Yan, Xinlei Zhou, Xizhao Wang, and Ran Wang*. An off-center technique: Learning a feature transformation to improve the performance of clustering and classification. Information Sciences, 359: 139–152 (2019). (中科院大类2区,小类1区top)

• Farhad Pourpanah, Ran Wang*, Chee Peng Lim, Xizhao Wang, Manjeevan Seera, and Choo Jun Tan. An improved fuzzy ARTMAP and Q-Learning agent model for pattern classification. Neurocomputing, 503: 635–651 (2019). (中科院大类2区)

• Xi-Zhao Wang, Tianlun Zhang, and Ran Wang*. Noniterative deep learning: Incorporating restricted boltzmann machine into multilayer random weight neural networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(7): 1299—1308 (2019). (中科院大类2区，小类1区top)

• Yan Lyu*, Chi-Yin Chow, Ran Wang, and Victor C.S. Lee. iMCRec: A multi-criteria framework for personalized point-of-interest recommendations. Information Sciences, 483:294–312 (2019). (中科院大类2区，小类1区top)

• Ran Wang*, Haoran Xie, Jiqiang Feng, Fu Lee Wang, and Chen Xu. Multi-criteria decision making based architecture selection for single-hidden layer feedforward neural networks. International Journal of Machine Learning and Cybernetics, 10(4): 65–666 (2019). (中科院大类3区)

• Yuheng Jia, Sam Kwong*, Wenhui Wu, Ran Wang, and Wei Gao. Sparse bayesian learning based kernel poisson regression. IEEE Transactions on Cybernetics, 49(1): 56–68 (2019). (中科院大类1区top)

• Zhiqi Huang, Ran Wang*, Hong Zhu, and Jie Zhu. Discovering the impact of hidden layer parameters on non-iterative training of feed-forward neural networks. Soft Computing, 22: 3495–3506 (2018). (中科院大类3区)

• Ran Wang*, Chi-Yin Chow, Yan Lyu, Victor C. S. Lee, Sam Kwong, Yanhua Li, and JiaZeng. TaxiRec: Recommending road clusters to taxi drivers using ranking-based extreme learning machines. IEEE Transactions on Knowledge and Data Engineering, 30(3): 585—598 (2018). (中科院大类2区, CCF A类)

• Xi-Zhao Wang, Ran Wang*, and Chen Xu. Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Transaction son Cybernetics, 48(2): 703—715 (2018). (中科院大类1区top)

• Ran Wang, Xi-Zhao Wang*, Sam Kwong, and Chen Xu. Incorporating diversity and informativeness in multiple-instance active learning. IEEE Transactions on Fuzzy Systems, 25(6): 1460—1475 (2017). (中科院大类1区top)

• Linwei Zhu, Yun Zhang*, Zhaoqing Pan, Ran Wang, Sam Kwong, and Zongju Peng. Binary and multi-class learning based low complexity optimization for HEVC encoding. IEEE Transactions on Broadcasting, 63(3): 547—561 (2017). (中科院大类2区)

• Ran Wang, Chi-Yin Chow, and Sam Kwong*. Ambiguity based multiclass active learning. IEEE Transactions on Fuzzy Systems, 24(1): 242—248 (2016). (中科院大类1区top)

• Ran Wang, Chi-Yin Chow*, Yan Lyu, Victor C. S. Lee, Sarana Nutanong, Yanhua Li,and Mingxuan Yuan. Exploring cell tower data dumps for supervised learning-based point-of-interest prediction. GeoInformatica, 20: 327—349 (2016). (中科院大类3区)

• Xiaodong Li*, Haoran Xie, Ran Wang, Yi Cai, Jingjing Cao, Feng Wang, Huaqing Min,and Xiaotie Deng. Empirical analysis: stock market prediction via extreme learning machine. Neural Computing and Applications, 27(1): 67–78 (2016). (中科院大类2区)

• Ran Wang, Sam Kwong*, Xi-Zhao Wang, and Qingshan Jiang. Segment based decision tree induction with continuous valued attributes. IEEE Transactions on Cybernetics, 45(7):1262–1275 (2015). (中科院大类1区top)

• Ran Wang, Yu-Lin He*, Chi-Yin Chow, Fang-Fang Ou, and Jian Zhang. Learning ELM-tree from big data based on uncertainty reduction. Fuzzy Sets and Systems, 258: 79–100 (2015). (中科院大类1区top)

• Yingjie Li, Ran Wang*, and Simon Chi Keung Shiu. Interval extreme learning machine for big data based on uncertainty reduction. Journal of Intelligent and Fuzzy Systems, 28:2391–2403 (2015). (中科院大类4区)

• Xu Wang*, Qiong Liu, Ran Wang, and Zhuo Chen. Natural Image Statistics based 3DReduced Reference Image Quality Assessment in Contourlet Domain. Neurocomputing, 151: 683–691 (2015). (中科院大类2区)

• Jingjing Cao, Sam Kwong*, Ran Wang, Xiaodong Li, Ke Li, and Xiangfei Kong. Class-specific soft voting based multiple extreme learning machines ensemble. Neurocomputing, 149: 275–284 (2015). (中科院大类2区)

• Ran Wang, and Sam Kwong*. Active learning with multi-criteria decision making systems. Pattern Recognition, 47(9): 3106–3119 (2014). (中科院大类2区)

• Ran Wang, Degang Chen, and Sam Kwong*. Fuzzy rough set based active learning. IEEE Transactions on Fuzzy Systems, 22(6): 1699–1704 (2014). (IF: 8.415, 中科院大类1区top)

• Xi-Zhao Wang, Ran Wang*, Hui-Min Feng, and Hua-Chao Wang. A new approach to classifier fusion based on upper integral. IEEE Transactions on Cybernetics, 44(5):620–635 (2014). (中科院大类1区top)

• Debby Dan Wang*, Ran Wang, and Hong Yan. Fast prediction of protein-protein interaction sites based on extreme learning machines. Neurocomputing, 128: 258–266 (2014). (中科院大类2区)

• Yu-Lin He*, Ran Wang, Sam Kwong, and Xi-Zhao Wang. Bayesian classifiers basedon probability density estimation and their applications to simultaneous fault diagnosis. Information Sciences, 259: 252–268 (2014). (中科院小类1区top)

• Hao Gao, Sam Kwong*, Baojie Fan, and Ran Wang. A hybrid particle-swarm tabusearch algorithm for solving job shop scheduling problems. IEEE Transactions on Industrial Electronics, 10(4): 2044–2054 (2014). (中科院大类1区top)

• Ke Li, Qingfu Zhang, Sam Kwong*, Miqing Li, and Ran Wang. Stable matching based selection in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 18(6): 909–923 (2014). (中科院大类1区top)

• Ran Wang*, Sam Kwong, and Debby Dan Wang. An analysis of ELM approximate error based on random weight matrix. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 21(suppl.2): 1–12 (2013). (中科院大类4区)

• Ran Wang, Sam Kwong*, Degang Chen, and Jingjing Cao. A vector-valued support vector machine model for multiclass problem. Information Sciences, 235: 174–194 (2013). (中科院小类1区top)

• Ke Li*, Ran Wang, Sam Kwong, and Jingjing Cao. Evolving extreme learning machine paradigm with adaptive operator selection and parameter control. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 21(suppl.2): 143–154 (2013). (中科院大类4区)

• Ke Li, Sam Kwong*, Ran Wang, Kit-Sang Tang, and Kim-Fung Man. Learning paradigm based on jumping genes: A general framework for enhancing exploration in evolutionary multiobjective optimization. Information Sciences, 226: 1–22 (2013). (中科院小类1区top)

• Ran Wang*, Sam Kwong, and Xizhao Wang. A study on random weights between input and hidden layers in extreme learning machine. Soft Computing, 16(9): 1465–1475 (2012). (中科院大类3区)

• Ran Wang, Sam Kwong*, and Degang Chen. Inconsistency-based active learning for support vector machines. Pattern Recognition, 45(10): 3751–3767 (2012). (中科院大类2区)

• Jingjing Cao, Sam Kwong*, and Ran Wang. A noise-detection based AdaBoost algorithm for mislabeled data. Pattern Recognition, 45(12): 4451–4465 (2012). (中科院大类2区)

会议论文：

• Farhad Pourpanah, Ran Wang, Xizhao Wang, and Danial Yazdani. Feature Selection for Data Classification based on Binary Brain Storm Optimization. in 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS 2019).

• Farhad Pourpanah, Ran Wang*, Xizhao Wang, Yuhui Shi and Danial Yazdani. mBSO: A Multi-Population Brain Storm Optimization for Multimodal Dynamic Optimization Problems. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019).

• Ran Wang and Suhe Ye. Multi-label active learning driven by uncertainty and inconsistency. in ICMLC 2019.

• Ran Wang, Sam Kwong, Yuheng Jia, Zhiqi Huang, and Lang Wu. Mutual information based K-labelsets ensemble for multi-label classification. in FUZZ-IEEE 2018.

• Yuheng Jia, Sam Kwong, Wenhui Wu, Wei Gao, and Ran Wang. Generalized relevance vector machine. in Intelligent Systems Conference 2017, pp. 638–645, 2017.

• Debby Dan Wang, Haoran Xie, Fu Lee Wang, Ran Wang, Xuefei Zhe, and Hong Yan. Biclustering-based iterative segmentation of human face images for facial feature extraction. in 10th IEEE Region 10 Conference (TENCON), pp. 1126-1129, 2016.

• Ran Wang, Chi-Yin Chow, Yan Lyu, Victor C. S. Lee, Sam Kwong, Yanhua Li, and Jia Zeng. TaxiRec: Recommending road clusters to taxi drivers using ranking-based extreme learning machines. in 23rd ACM SIGSPATIAL, article no. 53, 2015.

• Mengyuan Wu, Sam Kwong, Qingfu Zhang, Ke Li, Ran Wang, and Bo Liu. Two-level stable matching-based selection in MOEA/D.in 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1720—1725, 2015.

• Ran Wang, Sam Kwong, Qingshan Jiang, and Ka-Chun Wong. Active learning based on single-hidden layer feed-forward neural networks. in 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2158—2163, 2015.

• Yan Lv, Chi-Yin Chow, Ran Wang, and Victor C. S. Lee. Using multi-criteria decision making for personalized point-of-interest recommendations. in 22nd ACM SIGSPATIAL, pp. 461–464,2014.

• Ran Wang, Chi-Yin Chow, Sarana Nutanong, Yan Lv, Yanhua Li, Mingxuan Yuan, and Victor C. S. Lee. Exploring cell tower data dumps for supervised learning-based point-of-interest prediction. in 22nd ACM SIGSPATIAL, pp. 457–460,2014.

• Jingjing Cao, Sam Kwong, Ran Wang, and Ke Li. An indicator-based selection multi-objective evolutionary algorithm with preference for multi-class ensemble. in Proc. 2014 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 147–152, 2014.

• Ke Li, Sam Kwong, Ran Wang, Jingjing Cao, and Imre J. Rudas. Multi-objective differential evolution with self-navigation. in Proc. 2012 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 508–513, 2012.

• Ran Wang, Sam Kwong, Degang Chen, and Qiang He. Fuzzy rough sets based uncertainty measuring for stream based active learning. in Proc. 2012 Int. Conf. on Machine Learning and Cybernetics, vol. 1, pp. 282–288, 2012.

• Jingjing Cao, Sam Kwong, Ran Wang, and Ke Li. A weighted voting method using minimum square error based on extreme learning machine. in Proc. 2012 Int. Conf. on Machine Learning and Cybernetics, vol. 1, pp. 411–414, 2012.

• Ran Wang, Sam Kwong, and Degang Chen. A new method for multi-class support vector machines by training least number of classifiers. in Proc. 2011 Int. Conf. Machine Learning and Cybernetics, vol. 2, pp. 648–653, 2011.

• Ran Wang, Sam Kwong, and Qiang He. Active learning based on support vector machines. in Proc. 2010 IEEE Int. Conf. Systems, Man and Cybernetics, pp. 1312–1316, 2010.

• Ran Wang, and Sam Kwong. Sample selection based on maximum entropy for support vector machines. in Proc. 2010 Int. Conf. Machine Learning and Cybernetics, vol. 3, pp. 1390–1395, 2010.