吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (6): 1851-1858.doi: 10.13229/j.cnki.jdxbgxb20170455
WANG Li-min(),LIU Yang,SUN Ming-hui(),LI Mei-hui
摘要:
为了提升K阶依赖贝叶斯分类(KDB)模型的条件依赖表达能力,本文以 Markov blanket的特征提取思想为基本原则,降低特征属性间的条件独立性,根据贪婪搜索策略进行贝叶斯分类模型的结构学习。基于训练样本集构建宏观模型,基于测试样本构建微观模型,最终通过集成模型进行决策。针对UCI机器学习数据集进行交叉验证,实验结果分别从0-1损失、偏差和方差等角度证明了本文算法的合理性和有效性。
中图分类号:
[1] |
Das M, Ghosh S, Gupta P , et al. Forward: a model for forecasting reservoir water dynamics using spatial Bayesian network (SpaBN)[J]. IEEE Transactions on Knowledge and Data Engineering, 2017,29(4):842-855.
doi: 10.1109/TKDE.2016.2647240 |
[2] |
Kang Z, Yang J, Zhong R . A Bayesian-network-based classification method integrating airborne LiDAR data with optical images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017,10(4):1651-1661.
doi: 10.1109/JSTARS.2016.2628775 |
[3] |
Fan X B, Li X . Network tomography via sparse Bayesian learning[J]. IEEE Communications Letters, 2017,21(4):781-784.
doi: 10.1109/LCOMM.2017.2649494 |
[4] |
Chen S L, Martinez A M, Webb G I , et al. Sample-based attribute selective AnDE for large data[J]. IEEE Transactions on Knowledge and Data Engineering, 2017,29(1):172-185.
doi: 10.1109/TKDE.2016.2608881 |
[5] |
Domingos P, Pazzani M . On the optimality of the simple bayesian classifier under zero-one loss[J]. Machine Learning, 1997,29(2):103-130.
doi: 10.1023/A:1007413511361 |
[6] | Hand D J, Yu K . Idiot's Bayes—not so stupid after all?[J]. International Statistical Review, 2001,69(3):385-398. |
[7] |
Friedman N, Dan G, Goldszmidt M . Bayesian network classifiers[J]. Machine Learning, 1997,29(2):131-163.
doi: 10.1023/A:1007465528199 |
[8] |
Jiang L X, Cai Z H, Wang D H , et al. Improving tree augmented naive Bayes for class probability estimation[J]. Knowledge-Based Systems, 2012,26:239-245.
doi: 10.1016/j.knosys.2011.08.010 |
[9] | Sahami M . Learning limited dependence Bayesian classifiers[EB/OL].[2017-04-25].. |
[10] |
Wang L, Zhao H, Sun M , et al. General and local: averaged k-dependence bayesian classifiers[J]. Entropy, 2015,17(6):4134-4154.
doi: 10.3390/e17064134 |
[11] |
Jiang Liang-xiao, Li Chao-qun, Wang Sha-sha , et al. Deep feature weighting for naive Bayes and its application to text classification[J]. Engineering Applications of Artificial Intelligence, 2016,52:26-39.
doi: 10.1016/j.engappai.2016.02.002 |
[12] |
Meehan A, de Campos C P . Averaged extended tree augmented naive classifier[J]. Entropy, 2015,17(7):5085-5100.
doi: 10.3390/e17075085 |
[13] | Martinez A M, Webb G I, Chen S , et al. Scalable learning of Bayesian network classifiers[J]. Journal of Machine Learning Research, 2016,17:1-35. |
[14] | Wang S C, Xu G L, Du R J . Restricted Bayesian classification networks[J]. Science China Information Sciences, 2013,56(7):1-15. |
[15] |
Vergara J R, Estévez P A . A review of feature selection methods based on mutual information[J]. Neural Computing and Applications, 2014,24(1):175-186.
doi: 10.1007/s00521-013-1368-0 |
[16] | Koller D, Sahami M. Toward optimal attribute selection [C]//In Proceedings of the 13th International Conference on Machine Learning,Bari, Italy, 1996: 284-292. |
[17] |
Aliferis C F, Statnikov A, Tsamardinos I , et al. Local causal and markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation[J]. Journal of Machine Learning Research, 2010,11:171-234.
doi: 10.1007/s11430-007-0106-9 |
[18] | Sechidis K, Brown G . Markov blanket discovery in positive-unlabelled and semi-supervised data[DB/OL].[2017-04-26].. |
[19] |
Wang L M, Yuan S M . Induction of hybrid decision tree based on post-discretization strategy[J]. Progress in Natural Science, 2004,14(6):541-545.
doi: 10.1080/10020070412331343911 |
[20] | MacKay D J . Information Theory, Inference, and Learning Algorithms[M]. Cambridge: Cambridge University Press, 2003. |
[21] | Breiman L . Bagging predictors[J]. Machine Learning, 1996,24(2):123-140. |
[22] | Friedman J, Hastie T, Tibshirani R . The Elements of Statistical Learning[M]. Berlin: Springer, 2009. |
[23] | Fayyad U M, Irani K B . Multi-interval discretization of continuous-valued attributes for classification learning[DB/OL].[2017-04-28].. |
[24] | Hu B, Rakthanmanon T, Hao Y , et al. Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL[M]. Berlin: Springer Berlin Heidelberg, 2013. |
[25] |
Demsar J . Statistical comparisons of classifiers over multiple data sets[J]. Journal of Machine Learning Research, 2006,7(1):1-30.
doi: 10.1007/s10846-005-9016-2 |
[26] | Ishitaki T, Oda T, Barolli L. A neural network based user identification for tor networks: data analysis using friedman test [C]//30th International Conference on Advanced Information Networking and Applications Workshops,Crans-Montana, Switzerland, 2016: 16022267. |
[1] | 王楠,李金宝,刘勇,张玉杰,钟颖莉. TPR⁃TF:基于张量分解的时间敏感兴趣点推荐模型[J]. 吉林大学学报(工学版), 2019, 49(3): 920-933. |
[2] | 刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850. |
[3] | 金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866. |
[4] | 赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872. |
[5] | 刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878. |
[6] | 欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570. |
[7] | 刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599. |
[8] | 车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628. |
[9] | 桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605. |
[10] | 刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613. |
[11] | 孙晓颖, 扈泽正, 杨锦鹏. 基于分层贝叶斯网络的车辆发动机系统电磁脉冲敏感度评估[J]. 吉林大学学报(工学版), 2018, 48(4): 1254-1264. |
[12] | 曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243. |
[13] | 赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223. |
[14] | 黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230. |
[15] | 傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236. |
|