Large-Scale Hierarchical Classification Online Streaming Feature Selection Based on Neighborhood Rough Set
BAI Shengxing1,2, LIN Yaojin1,2, WANG Chenxi1,2, CHEN Shengyu3
1.School of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000 2.Fujian Key Laboratory of Granular Computing and Application, Zhangzhou 363000 3.School of Informatics, Computing & Engineering, Indiana University Bloomington, Bloomington 47408, USA
Abstract Label space of data possesses a hierarchical structure, and feature space is unknown and evolutionary in many classification learning tasks. An online streaming feature selection framework for large-scale hierarchical classification task is proposed. Firstly, a neighborhood rough model is defined for hierarchical structure data. Important features are dynamically selected based on feature correlation. Finally, the redundant dynamic features are identified based on feature redundancy. Experiments are conducted to verify the effectiveness of the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China (No.61672272), Natural Science Foundation of Fujian Provin-ce(No.2018J01548, 2018J01547), Department of Education of Fujian Province(No.JT180318)
Corresponding Authors:
LIN Yaojing, Ph.D., professor. His research interests include data mining and granular computing.
About author:: BAI Shengxing,master student. His research interests include data mining;WANG Chenxi, master, lecturer. Her research interests include data mining;CHEN Shengyu, master student. His research interests include data mining.
[1] 胡清华,王 煜,周玉灿,等.大规模分类任务的分层学习方法综述.中国科学:信息科学, 2018, 48(5): 487-500. (HU Q H, WANG Y, ZHOU Y C, et al. A Review on Hierarchical Learning Methods for Large-Scale Classification Task. Scientia Sinica(Informationis), 2018, 48(5): 487-500.) [2] GOPAL S, YANG Y M. Hierarchical Bayesian Inference and Recursive Regularization for Large-Scale Classification. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3). DOI: 10.1145/2629585. [3] ASHBURNER M, BALL C A, BLAKE J A, et al. Gene Ontology: Tool for the Unification of Biology. Nature Genetics, 2000, 25(1): 25-29. [4] NOURANI-VATANI N, LÓPEZ-SASTRE R, WILLIAM S. Structured Output Prediction with Hierarchical Loss Functions for Seafloor Imagery Taxonomic Categorization // Proc of the Iberian Conference on Pattern Recognition and Image Analysis. Berlin, Germany: Springer, 2015: 173-183. [5] SUN A X, LIM E P. Hierarchical Text Classification and Evaluation // Proc of the 1st IEEE International Conference on Data Mining. Washington, USA: IEEE, 2001: 521-528. [6] 王晨曦,林耀进,唐 莉,等.基于信息粒化的多标记特征选择算法.模式识别与人工智能, 2018, 31(2): 123-131. (WANG C X, LIN Y J, TANG L, et al. Multi-label Feature Selection Based on Information Granulation. Pattern Recognition and Artificial Intelligence, 2018, 31(2): 123-131.) [7] FREEMAN C, KULIC/ D, BASIR O. Joint Feature Selection and Hierarchical Classifier Design // Proc of the IEEE International Conference on Systems, Man, and Cybernetics. Washington, USA: IEEE, 2011: 1728-1734. [8] SONG J, ZHANG P Z, QIN S J, et al. A Method of the Feature Selection in Hierarchical Text Classification Based on the Category Discrimination and Position Information. IEEE Transactions on Engineering Management, 2015, 53(4): 555-569. [9] ZHAO H, ZHU P F, WANG P, et al. Hierarchical Feature Selection with Recursive Regularization // Proc of the 26th International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2017: 3483-3489. [10] WU X D, YU K, DING W, et al. Online Feature Selection with Streaming Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5): 1178-1192. [11] ZHOU P, HU X G, LI P P, et al. OFS-Density: A Novel Online Streaming Feature Selection Method. Pattern Recognition, 2019, 86: 48-61. [12] 刘景华,林梦雷,王晨曦,等.基于局部子空间的多标记特征选择算法.模式识别与人工智能, 2016, 29(3): 240-251. (LIU J H, LIN M L, WANG C X, et al. Multi-label Feature Selection Algorithm Based on Local Subspace. Pattern Recognition and Artificial Intelligence, 2016, 29(3): 240-251.) [13] LIN Y J, HU Q H, LIU J H, et al. Streaming Feature Selection for Multilabel Learning Based on Fuzzy Mutual Information. IEEE Transactions on Fuzzy Systems, 2017, 25(6): 1491-1507. [14] LIU J H, LIN Y J, LI Y W, et al. Online Multi-label Streaming Feature Selection Based on Neighborhood Rough Set. Pattern Reco-gnition, 2018, 84: 273-287. [15] 介 飞,谢 飞,李 磊,等.社交网络中隐式事件突发性检测.自动化学报, 2018, 44(4): 730-742. (JIE F, XIE F, LI L, et al. Latent Event-Related Burst in Social Networks. Acta Automatica Sinica, 2018, 44(4): 730-742.) [16] WU F H, ZHANG J, HONAVAR V. Learning Classifiers Using Hierarchically Structured Class Taxonomies // Proc of the International Symposium on Abstraction, Reformulation and Approximation. Berlin, Germany: Springer, 2005: 313-320. [17] JR SILLA C N, FREITAS A A. A Survey of Hierarchical Classification across Different Application Domains. Data Mining and Knowledge Discovery, 2011, 22(1/2): 31-72. [18] 胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简.软件学报, 2008, 19(3): 640-649. (HU Q H, YU D R, XIE Z X. Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation. Journal of Software, 2008, 19(3): 640-649.) [19] EISNER R, POULIN B, SZAFRON D, et al. Improving Protein Function Prediction Using the Hierarchical Structure of the Gene Ontology // Proc of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. Washington, USA: IEEE, 2005. DOI: 10.1109/CIBCB.2005.1594940. [20] CECI M, MALERBA D. Classifying Web Documents in a Hierarchy of Categories: A Comprehensive Study. Journal of Intelligent Information Systems(Integrating Artificial, Intelligence and Database Technologies), 2007, 28(1): 37-78. [21] KRIZHEVSKY A, HINTON G. Learning Multiple Layers of Features from Tiny Images[C/OL]. [2019-04-25]. http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf. [22] LAMPERT C H, NICKISCH H, HARMELING S. Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer // Proc of the IEEE Conference on Computer Vision and Pattern Reco-gnition. Washington, USA: IEEE, 2009: 951-958. [23] EVERINGHAM M, VAN GOOL L, WILLIAMS C K, et al. The Pascal Visual Object Classes(voc) Challenge. International Journal of Computer Vision, 2010, 88(2): 303-338. [24] WEI L Y, LIAO M H, GAO X, et al. An Improved Protein Structural Classes Prediction Method by Incorporating both Sequence and Structure Information. IEEE Transactions on NanoBioscience, 2015, 14(4): 339-349. [25] DING C H, DUBCHAK I. Multi-class Protein Fold Recognition Using Support Vector Machines and Neural Networks. Bioinforma-tics, 2001, 17(4): 349-358. [26] DEKEL O, KESHET J, SINGER Y. Large Margin Hierarchical Classification // Proc of the 21th ACM International Conference on Machine Learning. New York, USA: ACM, 2004. DOI: 10.1145/1015330.1015374. [27] YU K, WU X D, DING W, et al. Scalable and Accurate Online Feature Selection for Big Data. ACM Transactions on Knowledge Discovery from Data, 2016, 11(2). DOI: 10.1145/2976744. [28] ZHOU P, HU X G, LI P P. A New Online Feature Selection Method Using Neighborhood Rough Set // Proc of the 8th IEEE Internatio- nal Conference on Big Knowledge. Washington, USA: IEEE, 2017: 135-142.
2019, Vol. 32 
