序号

论文著录

[1]

张志东，王志海，刘海洋，等．一种基于树型贝叶斯网络的集成多标记分类算法[J]．计算机科学, Volume 45, Number 3, 2018. pp. 189-195.

[2]

孙艳歌，王志海．一种面向不平衡数据流的集成分类算法. 小型微型计算机系统. Volume 39, Number 6, 2018. pp. 1178-1183.

[3]

Sun Y, Wang Z, Bai Y, et al. A Classifier Graph Based Recurring Concept Detection and Prediction Approach[J]. Computational Intelligence and Neuroscience，Volume 2018, Article ID 4276291. pp. 1-13.

[4]

张伟，王志海，原继东，刘海洋．一种局部属性加权朴素贝叶斯分类算法 [J]. 北京交通大学学报, Volume 42, Number 2, 2018. pp. 14-21.

[5]

Jidong Yuan, Zhihai Wang, Yange Sun, Wei Zhang and Jingjing Jiang. An effective pattern-based Bayesian classifier for evolving data stream. Neurocomputing, Volume 295, 2018. pp. 17-28

[6]

Zhuoya Ju, Zhihai Wang, and Shiqiang Wang. A lazy one-dependence classification algorithm based on selective patterns. In: Xin Geng, and Byeong-Ho Kang. (eds.). In: Xin Geng, and Byeong-Ho Kang. (eds.). PRICAI 2018: Trends in Artificial Intelligence (PRICAI 2018). Springer, 2018. pp: 1-8.

[7]

艾丽华，于双元，徐薇，王志海．关注目标的课程建设探索与实践[J]．计算机教育, Number 6, 2017. pp. 55-57.

[8]

白洋，王志海，孙艳歌，等．基于图的概念重现发现与预测[J]．郑州大学学报(工学版), Volume 38, Number 4, 2017. pp. 57-64.

[9]

江晶晶，王志海，原继东．一种基于数据流模式表示的半懒惰式分类算法[J]．计算机科学, Volume 44, Number 7, 2017. pp. 167-174.

[10]

刘春爽，王志海．计算机软件工程管理与应用解析[J]．通讯世界, Volume 19, 2017. pp. 26-27.

[11]

李兴中，王志海，尹洪峰．常用计算机编程语言和选用技巧探析[J]．信息与电脑, Volume 17, 2017. pp. 58 -59.

[12]

孙艳歌，王志海，黄丹．基于时间的局部低秩张量分解的协同过滤推荐算法[J]．计算机科学, Volume 44, Number 7, 2017. pp. 227-231.

[13]

Jidong Yuan, Zhihai Wang, Yange Sun and Wei Zhang: A pattern-based Bayesian classifier for data stream, 24th International Conference On Neural Information Processing (ICONIP 2017). Guangzhou, China, 2017. pp. 868-877.

[14]

原继东，王志海，孙艳歌，张伟．面向复杂时间序列的k近邻分类器．软件学报, Volume 28, Number 11, 2017. pp. 3002-3017.

[15]

Sun Y, Wang Z, Li H, et al. A novel ensemble classification for data streams with class imbalance and concept drift[J]. International Journal of Performability Engineering, Volume 13, Number 6, 2017. pp. 945-955..

[16]

孙艳歌，王志海，原继东．基于信息熵的数据流自适应集成分类算法，中国科技大学学报. Volume 47, Number 7, 2017, 47. pp. 575-582.

[17]

杜超，王志海，江晶晶,等．基于显露模式的数据流贝叶斯分类算法[J]. 软件学报, Volume 28, Number 11, 2017. pp. 2891-2904.

[18]

顾静秋，王志海，高荣华，吴华瑞．融合图像与运动量的奶牛行为识别方法[J]. 农业机械学报，2017，48(6): 145-151

[19]

Jingqiu Gu, Zhihai Wang, Ronghua Gao, Huarui Wu. Cow behavior recognition based on image analysis and activities[J]. International Journal of Agricultural and Biological Engineering, Volume 10, Number 3, 2017. pp. 165-174.

[20]

韩萌，王志海，丁剑．一种频繁模式决策树处理可变数据流[J]. 计算机学报, Volume 39, Number 8, 2016, pp. 1541-1554.

[21]

孙艳歌，王志海等．数据流滑动窗口方式下的自适应集成算法[J]. 北京交通大学学报, Volume 40, Numbers 5, 2016. pp. 9-15.

[22]

Yange Sun, Zhihai Wang  and Haiyang Liu et al. Online ensemble using adaptive windowing for data streams with concept drift. International Journal of Distributed Sensor Networks. Volume 12, Number 5, 2016.pp.1-9.

[23]

Jingqiu Gu, Zhihai Wang, Huarui Wu. Recognition method of abnormal data based on interval estimation[C]. In: The Proceedings of the 2016 International Conference on Materials, Information, Communication, Mechanical and Electrical Engineering (MICMEE 2016), Lancaster, PA: DEStech Publications, Inc. 2016. PP. 212-218.

[24]

胡俊，王志海，于双元，等．工程教育认证与高校间协同培养的相关性研究[J]．计算机教育, Volume 19, 2015. pp. 27-31.

[25]

王树英，王志海．基于增量式决策树的时间序列分类算法研究[J]．现代计算机:专业版, Number 3, 2015. pp. 26-30.

[26]

Bin Fu, Guandong Xu, Longbing Cao, Zhihai Wang, and Zhiang Wu. Coupling multiple views of relations for recommendation[C]. In: The Proceedings of 19th Pacific-Asia Knowledge Discovery and Data Mining (PAKDD 2015, LNAI 9078). Ho Chi Minh, Vietnam: Springer, 2015. pp. 732-743.

[27]

韩萌，王志海，原继东．基于高斯函数的衰减因子设置方法研究[J]. 计算机研究与发展, Volume 53, Number 12, 2015. pp. 2834-2843.

[28]

Meng Han, Zhihai Wang, Jidong Yuan. Mining closed and multi-supports-based sequential pattern in high dimensional dataset[J]. International Arab Journal of Information Technology, Volume 12, Number 4, 2015. pp. 360-369.

[29]

韩萌，王志海，原继东．一种基于时间衰减模型的数据流闭合模式挖掘方法[J]. 计算机学报, Volume 38, Numbers 7, 2015. pp. 1473-1483.

[30]

刘海洋，王志海，黄丹等．基于评分矩阵局部低秩假设的成列协同排名算法[J]. 软件学报, Volume 26, Number 11, 2015. pp. 2981-2993.

[31]

原继东，王志海．时间序列的表示与分类算法综述[J]. 计算机科学, Volume 42, Numbers 3, 2015. pp.1-7.

[32]

原继东，王志海，韩萌．基于shapelets剪枝和覆盖的时间序列分类算法[J]. 软件学报, Volume 26, Numbers 9, 2015. pp. 2311-2325.

[33]

原继东，王志海，韩萌，游洋． 基于逻辑shapelets转换的时间序列分类算法[J]. 计算机学报, Volume 38, Numbers 7, 2015. pp.1448-1459.

[34]

Jidong Yuan, Zhihai Wang, Meng Han and Yange Sun. A lazy associative classifier for time series[J]. Intelligent Data Analysis, Volume 19, Number 5, 2015. pp. 983-1002.

[35]

范双燕，王志海，刘海洋．基于改进的FolkRank广告推荐及预测算法[J]．软件, Volume 35, Number 9, 2014. pp. 43-48.

[36]

张培倩，王志海．基于迭代加权线性模型的网络回归算法[J] ．计算机工程, Volume 40, Number 6, 2014. pp. 166-170.

[37]

何颖婧，王志海，李哲．基于标记集合划分的多标记分类算法[J]．昆明理工大学学报(自然科学版), Volume 39, Number 3, 2014. pp. 54-60.

[38]

Bin Fu, Zhihai Wang, and etal. Multi-label learning based on iterative label propagation over graph [J]. Pattern Recognition Letters, Volume 42, 2014. pp. 85-90.

[39]

Meng Han, Zhihai Wang, Jidong Yuan. Efficient method for mining patterns from highly similar and dense database based on prefix-frequent-items[J]. Journal of Software, Volume 9, Number 8, 2014. pp. 2080-2086.

[40]

Meng Han, Zhihai Wang, Yashu Liu. Selecting one dependency estimators in Bayesian network using different MDL Scores and overfitting criterion[J]. Journal of Information Science and Engineering, Volume 30, Number 2, 2014. pp. 371-385.

[41]

Jidong Yuan, Zhihai Wang, Meng Han. A discriminative shapelets transformation for time series classification[J]. International Journal of Pattern Recognition and Artificial Intelligence, Volume 28, Number 6, 2014. pp. 1450014:1-28.

[42]

李哲，王志海，何颖婧，等．一种启发式多标记分类器选择与排序策略[J]．中文信息学报, Volume 27, Number 4, 2013. pp. 119-126.

[43]

Bin Fu, Guandong Xu, Zhihai Wang, Longbing Cao. Leveraging supervised label dependency propagation for multi-label learning. In: The Proceedings of the 2013 IEEE 13th International Conference on Data Mining (ICDM2013). Dallas, USA, 2013. pp. 1061-1066.

[44]

Meng Han, Zhihai Wang, Jidong Yuan. Mining constraint-based sequential pattern and sequential rule on restaurant recommendation System[J]. Journal of Computational Information Systems, Volume 9, Issue 10, 2013. pp. 3901-3908.

[45]

Meng Han, Zhihai Wang, Jidong Yuan. Closed sequential pattern mining in high dimensional sequences[J]. Journal of Software, Volume 8, Issue 6, 2013. pp. 1368-1373.

[46]

Jidong Yuan, Zhihai Wang, Meng Han, Yashu Liu. Efficiently using different arrays in mining access patterns. Journal of Computational Information Systems, Volume 9, Number 5, 2013. pp.  1791-1798.

[47]

Jingqiu Gu, and Zhihai Wang. Design and implementation of agricultural knowledge cloud service [J]. Journal of Information Technology, Volume 12, Number 21, 2013. pp. 6208-6212.

[48]

徐薇，王志海．数据结构课程研究性教学理论及方法探索[J]．计算机教育, Volume 157, Number 1, 2012. pp. 35-38.

[49]

王中锋，王志海，解文杰．基于树型贝叶斯网络的场景分类引擎训练算法[J]．仪器仪表学报, Volume 33, Number 4, 2012. pp. 863-869.

[50]

刘志鑫，王志海．基于机器学习反馈的车辆自动路况识别[J]．计算机仿真, Volume 29, Number 1, 2012. pp. 339-343.

[51]

Bin Fu, Zhihai Wang, and Rong Pan et al.. An integrated pruning criterion for ensemble learning based on classification accuracy and diversity. In: The Proceedings of the 7th International KMO Conference on Knowledge Management, Services and Cloud computing (KMO2012), Salamanca, Spain, 2012. pp. 47-58.

[52]

Bin Fu, Zhihai Wang, and Rong Pan et al.. Learning tree structure of labels dependency for multi-label learning. In: The Proceedings of the 16th Pacific-Asia Knowledge Discovery and Data Mining (PAKDD 2012). Kuala Lumpur, Malaysia, 2012. pp. 159-170.

[53]

韩萌，王志海．采用信息增益排序的SuperParent算法的研究[J]. 计算机工程与设计, Volume 33, Numbers 10, 2012, pp. 3935-3939.

[54]

王中锋，王志海．基于条件对数似然函数导数的贝叶斯网络分类器优化算法[J]．计算机学报, Volume 52, Number 2, 2012．pp. 364 -374

[55]

Zhongfeng Wang, Zhihai Wang，Wenjie Xie, Tree-structured Bayesian network learning with application to scene classification[J], Electronics Letters, Volume 47, Number 9, 2011. pp. 540-541.

[56]

Zhongfeng Wang, Zhihai Wang, Bin Fu. A learning framework of tree-structured Bayesian network classifiers[J], Journal of Computational Information Systems, Volume 7, Number 9, 2011. pp. 2859-2866

[57]

付彬，王志海，王中锋．最大化边际的分类器选取算法．计算机科学与探索．Volume 5, Number 1, 2011. pp. 59-67．

[58]

付彬，王志海．基于树型依赖结构的多标记分类算法．模式识别与人工智能，Volume 25, Number 4, 2011. pp. 573-580.

[59]

王中锋，王志海，付彬．贝叶斯网络分类器结构与变量分布的差异性分析[J]．北京交通大学学报(自然科学版), Volume 35, Number 2, 2011. pp. 35-39.

[60]

Zhongfeng Wang, Zhihai Wang, Bin Fu. Learning robust Bayesian network classifiers in the space of Markov equivalent classes. In: Dai, H. H. (ed.). Proceedings of the 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010). Sydney, Australia. 2010. pp. 891-898.

[61]

Zhongfeng Wang, Zhihai Wang, Bin Fu. Learning restricted Bayesian network classifiers with mixed non-i.i.d. sampling[C]. In: Dai, H. H. (ed.). Proceedings of the 10th IEEE International Conference on Data Mining Workshops, (ICDMW 2010), Sydney, Australia. 2010: 899-904.

[62]

王中锋，王志海，付彬．一种贝叶斯网络分类器集群式参数学习的降噪算法[J]．模式识别与人工智能, Volume 23, Number 4, 2010. pp. 508-515．

[63]

王中锋，王志海，付彬．一种局部打分搜索型限制性贝叶斯网络结构学习算法[J]．南京大学学报 (自然科学版), Volume 45, Number 5, 2009. pp. 654-664.

[64]

付彬，王志海，王中锋．Boosting算法中基分类器权重的动态赋值[J]．广西师范大学学报(自然科学版), Volume 27, Number3, 2009. pp. 85-88．

[65]

王学玲, 王志海, 王建林. 基于有向树算法构造的TAN分类器[J]. 计算机工程与设计, Volume 29, Number 13, 2008. pp. 3451-3453.

[66]

王建林，王志海，王学玲．一种懒惰式决策树和普通决策树结合的分类模型——半懒惰式决策树[J]．计算机应用与软件, Volume 25, Number 12, 2008. pp. 229-230.

[67]

李锦善，王志海，王中锋．一种基于假设检验的贝叶斯分类器[J]．计算机工程与应用, Volume 44, Number 21, 2008. pp. 222-224.

[68]

王建林，王志海，王学玲．基于不完全数据的TAN学习算法[J]．计算机工程与应用, Volume 43, Number 36, 2007. pp. 181-184.

[69]

李广群，王志海，田凤占．一种基于AdaBoost方法的树形HNB组合分类器[J]．广西师范大学学报(自然科学版), Volume 25, Number 4, 2007. pp. 164-167.

[70]

石洪波，黄厚宽，王志海．基于Boosting的TAN组合分类器[J] ．计算机研究与发展, Volume 41, Number 2, 2004. pp. 340-345.

[71]

石洪波，王志海，黄厚宽，等．一种限定性的双层贝叶斯分类模型[J]．软件学报, Volume 15, Number 2, 2004. pp. 193-199.

[72]

王志海，张璠．一种基于粗糙集合理论的树扩张型贝叶斯网络分类器[J]. 复旦学报(自然科学版), Volume 43, Number 5, 2004. pp. 725-728.