您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (2): 60-65.doi: 10.6040/j.issn.1672-3961.0.2019.760

• 机器学习与数据挖掘 • 上一篇    下一篇

符号序列的LDA主题特征表示方法

冯超1,2(),徐鲲鹏1,2,陈黎飞1,2,*()   

  1. 1. 福建师范大学数学与信息学院，福建 福州 350117
    2. 数字福建环境监测物联网实验室，福建 福州 350117
  • 收稿日期:2019-12-18 出版日期:2020-04-20 发布日期:2020-04-16
  • 通讯作者: 陈黎飞 E-mail:fc_fight2017@163.com;clfei@fjnu.edu.cn
  • 作者简介:冯超(1994—)，男，安徽六安人，硕士研究生，主要研究方向为数据挖掘. E-mail：fc_fight2017@163.com
  • 基金资助:
    国家自然科学基金资助项目(61672157);国家自然科学基金资助项目(U1805263);福建师范大学创新团队资助项目(IRTL1704)

LDA-based topic feature representation method for symbolic sequences

Chao FENG1,2(),Kunpeng XU1,2,Lifei CHEN1,2,*()   

  1. 1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, Fujian, China
    2. Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fuzhou 350117, Fujian, China
  • Received:2019-12-18 Online:2020-04-20 Published:2020-04-16
  • Contact: Lifei CHEN E-mail:fc_fight2017@163.com;clfei@fjnu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61672157);国家自然科学基金资助项目(U1805263);福建师范大学创新团队资助项目(IRTL1704)

RichHTML

6

PDF (PC)

483

摘要：

针对现有序列挖掘算法特征维度高、学习算法时间复杂度高等方面的不足，提出一种主题特征表示法，将符号序列转换为一组表示多个主题呈现度的概率向量。基于文本挖掘中常用的隐含狄利克雷分配(latent Dirichlet allocation, LDA)主题模型，视短序列元组为序列的浅层特征(词)，利用LDA模型学习算法提取主题及其概率分布，作为序列的深层特征。在6个实际序列数据集上进行试验，并与基于元组、Markov模型的现有方法作对比，结果表明，新方法在降低特征维度的同时提高了表示模型的学习效率，在符号序列分类应用中可以取得较理想的分类精度。

关键词: 特征表示, 符号序列, 隐含狄利克雷分配, 主题, 分类

Abstract:

To address the problems of high feature dimensionality and high algorithm time complexity in the existing methods, a topic feature representation method was proposed to transform the symbolic sequences into a set of topic probability vectors, based on the topic model latent Dirichlet allocation (LDA) commonly used in text mining. In the new method, each short sequence gram was considered as the shallow feature (word) of the sequence, and the topics with their probability distributions were extracted as the deep features of the sequences using the LDA model learning algorithm.Experiments were carried out on six real-world sequence sets, and compared with the existing grams-based and Markov model-based methods. The results showed that the new method improved the learning efficiency of the representation model while reducing the feature dimensionality, and achieved better accuracy in the application of symbolic sequence classification.

Key words: feature representation, symbolic sequences, latent Dirichlet allocation, topics, classification

中图分类号: 

  • TP311

图1

SLDA模型示意图"

表1

试验数据集的参数汇总表"

数据集 序列数M 类别数目C 平均序列长度 平均符号数目
GS1 771 8 1 594 4
GS2 281 6 1 318 4
GS3 310 6 1 536 4
SS1 50 5 1 899 15
SS2 50 5 1 498 16
SS3 50 5 925 15

图2

F1随SLDA主题数的变化图"

表2

符号序列集上不同方法提取的特征数目及分类结果F1指标对比"

数据集 SLDA 特征数目 MM-FR 特征数目 G-FR 特征数目
GS1 0.946 8 35 0.911 8 16 0.980 5 81
GS2 1.000 0 15 0.996 4 16 0.996 4 85
GS3 0.967 7 35 0.922 6 16 0.996 8 72
SS1 1.000 0 15 1.000 0 324 0.980 0 227 7
SS2 1.000 0 5 1.000 0 400 1.000 0 252 9
SS3 1.000 0 5 1.000 0 400 1.000 0 212 1
1 DONG G , PEI J . Sequence data mining[M]. Berlin: Springer, 2007: Ⅶ- 69.
2 BENGIO Y , COURVILLE A , VINCENT P . Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intellig-ence, 2013, 35 (8): 1798- 1828.
doi: 10.1109/TPAMI.2013.50
3 COVER T M , HART P E . Nearest neighborpatternclassification[J]. IEEE Transactions on Information Theory, 1967, 13 (1): 21- 27.
4 XING Z , PEI J , KEOGH E J . A brief survey on sequence classification[J]. ACM SIGKDD Explorations Newsletter, 2010, 12 (1): 40- 48.
doi: 10.1145/1882471.1882478
5 BLASIAK S, RANGWALA H. A hidden Markov model variant for sequence classification[C]//Proceedings of the 22nd International Joint Conference on Artificial Intelligence. Barcelona, Catalonia, Spain: IJCAI, 2011: 1192-1197.
6 郭彦明.基于隐马尔可夫模型的DNA序列分类研究[D].福州:福建师范大学, 2015.
GUO Yanming. A study of DNA sequence classification based on hidden Markov model[D]. Fuzhou: Fujian Normal University, 2015.
7 GUO G , CHEN L , YE Y , et al. Cluster validation method for determining the number of clusters in categorical sequences[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28 (12): 2936- 2948.
doi: 10.1109/TNNLS.2016.2608354
8 YUAN L , WANG W , CHEN L . Two-stage pruning method for gram-based categorical sequence clustering[J]. International Journal of Machine Learning and Cybernetics, 2019, 10 (4): 631- 640.
doi: 10.1007/s13042-017-0744-y
9 GERS F A , SCHMIDHUBER J , Cummins F . Learning toforget: continual prediction with LSTM[J]. Neural Computation, 2000, 12 (10): 2451- 2471.
doi: 10.1162/089976600300015015
10 GREFF K , SRIVASTAVA R K , KOUTNIK J , et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28 (10): 2222- 2232.
doi: 10.1109/TNNLS.2016.2582924
11 GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks[C]//Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing. Vancouver, Canada: IEEE, 2013: 6645-6649.
12 TANG D, QIN B, LIU T. Document modeling with gated recurrent neural network for sentiment classification[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal: ACL, 2015: 1422-1432.
13 JEBARA T , KONDOR R I , HOWARD A . Probability product kernels[J]. Journal of Machine Learning Research, 2004, 5 (5): 819- 844.
14 SALTON G . A vector space model for automatic indexing[J]. Communications of the ACM, 1975, 18 (11): 613- 620.
doi: 10.1145/361219.361220
15 XIONG T, WANG S, JIANG Q. A new Markov Model for clustering categorical sequences[C]//Proceedings of the 11th IEEE International Conference on Data Mining. Vancouver, Canada: IEEE Computer Society, 2011: 854-863.
16 程铃钫, 郭躬德, 陈黎飞. 符号序列多阶Markov分类[J]. 计算机应用, 2017, 37 (7): 1977- 1982.
CHENG Lingfang , GUO Gongde , CHEN Lifei . Classification of symbolic sequences with multi-order Markov Model[J]. Journal of Computer Applications, 2017, 37 (7): 1977- 1982.
17 郭彦明, 陈黎飞, 郭躬德. 基于隐马尔科夫模型的DNA序列分类方法[J]. 计算机系统应用, 2014, 23 (7): 24- 30.
doi: 10.3969/j.issn.1003-3254.2014.07.005
GUO Yanming , CHEN Lifei , GUO Gongde . DNA sequence classification method based on Hidden Markov Model[J]. Computer Systems & Applications, 2014, 23 (7): 24- 30.
doi: 10.3969/j.issn.1003-3254.2014.07.005
18 周玉元, 周铁军. DNA序列分类的Fisher判别法[J]. 湖南农业大学学报(自然科学版), 2003, 29 (5): 437- 440.
ZHOU Yuyuan , ZHOU Tiejun . The Fisher criterion on classification of the DNA sequence[J]. Journal of Hunan Agricultural University (Natural Sciences), 2003, 29 (5): 437- 440.
19 DAI A M , LE Q V . Semi-supervised sequence learning[J]. Advances in Neural Information Processing Systems, 2015, 3079- 3087.
20 GRAVES A, JAITLY N, MOHAMED A R. Hybrid speech recognition with deep Bidirectional LSTM[C]//Proceedings of 2013 IEEE Workshop on Automatic Speech Recognition and Understanding. Olomouc, Czech Republic: IEEE, 2013: 273-278.
21 DEERWESTER S , DUMAIS S T , LANDAUER T K , et al. Indexing by Latent Semantic Analysis[J]. Journal of the American Society for Information Science, 1990, 41 (6): 391- 407.
doi: 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
22 THOMAS H. Probabilisticlatent semantic analysis[C]//Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence. Stockholm, Sweden: Morgan Kaufmann, 1999: 289-296.
23 BLEI D M , NG A Y , JORDAN M I . Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3, 993- 1022.
24 GRIFFITHS T L , STEYVERS M . Finding scientific topics[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101 (1): 5228- 5235.
25 KELIL A, WANG S. SCS: a new similarity measure for categorical sequences[C]// Proceedings of the 8th IEEE International Conference on Data Mining. Pisa, Italy: IEEE Computer Society, 2008: 343-352.
26 WEI D , JIANG Q , WEI Y , et al. A novel hierarchical clustering algorithm for gene sequences[J]. BMC Bioinformatics, 2012, 13 (1): 174- 186.
[1] 宋士奇,朴燕,蒋泽新. 基于改进YOLOv3的复杂场景车辆分类与跟踪[J]. 山东大学学报 (工学版), 2020, 50(2): 27-33.
[2] 李春阳,李楠,冯涛,王朱贺,马靖凯. 基于深度学习的洗衣机异常音检测[J]. 山东大学学报 (工学版), 2020, 50(2): 108-117.
[3] 陈馨菂,李天瑞,杨欢欢. 基于时间序列数据的交互式主题河流可视化[J]. 山东大学学报 (工学版), 2019, 49(4): 29-35, 43.
[4] 张红斌,邱蝶蝶,邬任重,朱涛,滑瑾,姬东鸿. 基于极端梯度提升树算法的图像属性标注[J]. 山东大学学报 (工学版), 2019, 49(2): 8-16.
[5] 高明霞,李经纬. 基于word2vec词模型的中文短文本分类方法[J]. 山东大学学报 (工学版), 2019, 49(2): 34-41.
[6] 侯霄雄,许新征,朱炯,郭燕燕. 基于AlexNet和集成分类器的乳腺癌计算机辅助诊断方法[J]. 山东大学学报 (工学版), 2019, 49(2): 74-79.
[7] 朱映雪,黄瑞章,马灿. 一种具有新主题偏向性的短文本动态聚类方法[J]. 山东大学学报 (工学版), 2018, 48(6): 8-18.
[8] 屈庆涛,刘其成,牟春晓. 基于N-Gram语言模型的并行自适应新闻话题追踪算法[J]. 山东大学学报 (工学版), 2018, 48(6): 37-43.
[9] 李尧,王志海,孙艳歌,张伟. 一种基于深度属性加权的数据流自适应集成分类算法[J]. 山东大学学报 (工学版), 2018, 48(6): 44-55, 66.
[10] 张璞,刘畅,王永. 基于特征融合和集成学习的建议语句分类模型[J]. 山东大学学报 (工学版), 2018, 48(5): 47-54.
[11] 王换,周忠眉. 一种基于聚类的过抽样算法[J]. 山东大学学报(工学版), 2018, 48(3): 134-139.
[12] 叶明全,高凌云,万春圆. 基于人工蜂群和SVM的基因表达数据分类[J]. 山东大学学报(工学版), 2018, 48(3): 10-16.
[13] 曹雅,邓赵红,王士同. 基于单调约束的径向基函数神经网络模型[J]. 山东大学学报(工学版), 2018, 48(3): 127-133.
[14] 龙柏,曾宪宇,李徵,刘淇. 电商商品嵌入表示分类方法[J]. 山东大学学报(工学版), 2018, 48(3): 17-24.
[15] 谢志峰,吴佳萍,马利庄. 基于卷积神经网络的中文财经新闻分类方法[J]. 山东大学学报(工学版), 2018, 48(3): 34-39.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 陈瑞,李红伟,田靖. 磁极数对径向磁轴承承载力的影响[J]. 山东大学学报(工学版), 2018, 48(2): 81 -85 .
[2] 张英,郎咏梅,赵玉晓,张鉴达,乔鹏,李善评 . 由EGSB厌氧颗粒污泥培养好氧颗粒污泥的工艺探讨[J]. 山东大学学报(工学版), 2006, 36(4): 56 -59 .
[3] Yue Khing Toh1 ， XIAO Wendong2 ， XIE Lihua1 . 基于无线传感器网络的分散目标跟踪：实际测试平台的开发应用（英文）[J]. 山东大学学报(工学版), 2009, 39(1): 50 -56 .
[4] 赵延风1,2, 王正中1,2 ,芦琴1,祝晗英3 . 梯形明渠水跃共轭水深的直接计算方法[J]. 山东大学学报(工学版), 2009, 39(2): 131 -136 .
[5] 梁京芸,王明刚,柴家前,刘永庆 . 1.6-二-(N5-取代苯基-N1-二胍)己烷盐酸盐的合成和体外抗菌活性[J]. 山东大学学报(工学版), 2008, 38(3): 104 -107 .
[6] 孟健, 李贻斌, 李彬. 四足机器人跳跃步态控制方法[J]. 山东大学学报(工学版), 2015, 45(3): 28 -34 .
[7] 何东之, 张吉沣, 赵鹏飞. 不确定性传播算法的MapReduce并行化实现[J]. 山东大学学报(工学版), 0, (): 22 -28 .
[8] 黄乐建,王建明 . 稳定相容节点积分无网格法动力学分析[J]. 山东大学学报(工学版), 2007, 37(5): 68 -72 .
[9] 吴 皓,田国会,黄 彬 . 未知环境探测的多机器人协作策略研究[J]. 山东大学学报(工学版), 2008, 38(4): 27 -31 .
[10] 侯燕,杨猛. 高效解决复杂拓扑问题的显式界面追踪算法[J]. 山东大学学报(工学版), 2016, 46(4): 15 -20 .
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发