Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 402-408.doi: 10.11896/jsjkx.191100094
• Big Data & Data Science • Previous Articles Next Articles
ZHOU Yu, REN Qin-chai, NIU Hui-bin
CLC Number:
[1] SZALAY A,GRAY J.Drowning in data[OL].https://www.sciam.com/explorations /1999/. [2] FAYYAD U M,PIATETSKY-SHAPIRO G,SMYTH P.From data mining to knowledge discovery:an overview[M]//Advances in Knowledge Discovery and Data Mining.American Association for Artificial Intelligence,1996. [3] BLUM A L,LANGLEY P.Selection of relevant features and examples in machine learning[J].Artificial Intelligence,1997,97(1/2):245-271. [4] BARBU A,SHE Y,DING L,et al.Feature Selection with An-nealing for Computer Vision and Big Data Learning[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(2):272-286. [5] LIU Y,BI J W,FAN Z P.Multi-class sentiment classification:The experimental comparisons of feature selection and machine learning algorithms[J].Expert Systems with Applications,2017,80:323-339. [6] DASGUPTA A,DRINEAS P,HARB B,et al.Feature selection methods for text classification[C]//Acm Sigkdd International Conference on Knowledge Discovery & Data Mining.ACM,2007. [7] LIU H.Feature Selection for Knowledge Discovery and DataMining[M].Kluwer Academic Publishers,1998. [8] KIVINEN J,MANNILA H.The power of sampling in know-ledge discovery[C]//Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems.ACM,1994:77-85. [9] BALCÁZAR J,DAI Y,WATANABE O.A random samplingtechnique for training support vector machines[C]//International Conference on Algorithmic Learning Theory.Springer-Verlag,2001. [10] FERRAGUT E M,LASKA J.Randomized Sampling for Large Data Applications of SVM[C]//International Conference on Machine Learning & Applications.IEEE Computer Society,2012. [11] LEE Y J,MANGASARIAN O L.RSVM:reduced support vector machines[C]//SIAM International Conference on Data Mi-ning.2001. [12] LEE Y J,HUANG S Y.Reduced Support Vector Machines:A Statistical Theory[J].IEEE Transactions on Neural Networks,2007,18(1):1-13. [13] LI X,CERVANTES J,YU W.Fast classification for large data sets via random selection clustering and Support Vector Ma-chines[M].IOS Press,2012. [14] ZHANG L,GUO J.A Method for the Selection of TrainingSamples Based on Boundary Samples[J].Journal of Beijing Uni-versity of Posts and Telecommunications,2006,29(4):77-80. [15] ALMEIDA M B D,BRAGA A D P,BRAGA J P.SVM-KM:Speeding SVMs learning with a priori cluster selection and k-means[C]//Brazilian Symposium on Neural Networks.IEEE,2000. [16] LLOYD S P.Least squares quantization in PCM[J].IEEETrans,1982,28(2):129-137. [17] GUAN D,YUAN W,LEE Y K,et al.Improving supervisedlearning performance by using fuzzy clustering method to select training data[J].Journal of Intelligent & Fuzzy Systems,2008,19(4):321-334. [18] ZHOU Y,ZHU A F,ZHOU L,et al.Sample data selectionmethod for neural network classifiers[J].Journal of Huazhong University of Science and Technology(Natural Science Edition),2012,40(6):39-43. [19] PEDRYCZ W.From fuzzy sets to shadowed sets:Interpretation and computing[J].International Journal of Intelligent Systems,2010,24(1):48-61. [20] CHEN J,ZHANG C,XUE X,et al.Fast instance selection for speeding up support vector machines[J].Knowledge-Based Sys-tems,2013,45(3):1-7. [21] SHEN X J,MU L,LI Z,et al.Large-scale support vector machine classification with redundant data reduction[J].Neuro-computing,2016,172:189-197. [22] KANG J,RYU K R,KWON H C.Using Cluster-Based Sam-pling to Select Initial Training Set for Active Learning in Text Classification[C]//Pacific-asia Conference on Knowledge Discovery & Data Mining.Springer Berlin Heidelberg,2004. [23] XU Z,YU K,TRESP V,et al.Representative sampling for text classification using support vector machines[C]//European Conference on Ir Research.Springer-Verlag,2003. [24] VAPNIK V N,VAPNIK V.Statistical Learning Theory[J].John Wiley and Sons,Inc.,1998. [25] WAN C H,LEE L H,RAJKUMAR R,et al.A hybrid text classication approach with low dependency on parameter by integrating k-nearest neighbor and support vector machine[J].Expert Systems with Applications,2012,39(15):11880-11888. [26] MATEI R,POP P C,VÂLEAN H .Optical character recognition in real environments using neural networks and k-nearest neighbor[J].Applied Intelligence,2013,39(4):739-748. [27] GONZÁLEZ M,BERGMEIR C,TRIGUERO I,et al.On thestopping criteria for k-nearest neighbor in positive unlabeled time series classification problems[J].Information Sciences,2016,328:42-59. [28] HART B P E.The condensed nearest neighbor rule [J].IEEE Transactions on Information Theory,1968,14(3):515-516. [29] GATES G W.The reduced nearest neighbor rule (Corresp.)[J].IEEE Transactions on Information Theory,1972,18(3):431-433. [30] RITTER G L,WOODRUFF H B,LOWRY S R,et al.An algorithm for a selective nearest neighbor decision rule (Corresp.)[J].IEEE Transactions on Information Theory,1975,21(6):665-669. [31] DASARATHY B V.Minimal consistent set (MCS) identification for optimal nearest neighbor decision systems design[J].IEEE Transactions on Systems Man & Cybernetics,1994,24(3):511-517. [32] ANGIULLI F.Fast condensed nearest neighbor rule[C]//International Conference on Machine Learning.ACM,2005. [33] SHIN H,CHO S.Neighborhood Property-Based Pattern Selec-tion for Support Vector Machines[J].Neural Computation,2007,19(3):816-855. [34] LI J,WANG Y P.A Fast Neighbor Prototype Selection Algorithm Based on Local Mean and Class Global Information [J].Acta Automatica Sinica,2014,40(6):1116-1125. [35] WILSON D L.Asymptotic properties of nearest neighbor rules using edited data[J].IEEE Transactions on Systems Man & Cybernetics,1972,SMC-2(3):408-421. [36] TOMEK I.An Experiment with the Edited Nearest-NeighborRule[J].IEEE Transactions on Systems Man & Cybernetics,2007,SMC-6(6):448-452. [37] HATTORI K,TAKAHASHI M.A new edited k-nearest neighbor rule in the pattern classification problem[J].Pattern Recognition,1999,33(3):521-528. [38] SHI X X,HU X G,LIN Y J.K-nearest neighbor classification algorithm combined with mutual neighbors and credibility[J].Journal of Hefei University of Technology,2014,37(9):1055-1058. [39] YU G H.Instance Selection for Complex Classification[D].Tianjin:Tianjin University,2014. [40] LOPEZCHAU A,GARCIA L L,CERVANTES J,et al.DataSelection Using Decision Tree for SVM Classification[C]//IEEE International Conference on Tools with Artificial Intelligence.IEEE Computer Society,2012. [41] CERVANTES J,LAMONT F G,LÓPEZ-CHAU A,et al.Data selection based on decision tree for SVM classification on large data sets[J].Applied Soft Computing,2015,37(C):787-798. [42] YANG M H,AHUJA N.A Geometric Approach to Train Support Vector Machines[J].Proc.IEEE Conf. Computer Vision & Pattern Rec,2000,1(6):430-437. [43] CRISP D J,BURGES C J C.A geometric interpretation of ν-SVM classifiers[C]//International Conference on Neural Information Processing Systems.MIT Press,1999. [44] PENG X.Efficient geometric algorithms for support vector ma-chine classifier[C]//Sixth International Conference on Natural Computation.IEEE,2010. [45] LUO Y,YI W,HE D,et al.Fast reduction for large-scale trai-ning data set[J].Journal of Southwest Jiaotong University,2007,42(4):468-460. [46] LIU C,WANG W,WANG M,et al.An efficient instance selection algorithm to reconstruct training set for support vector machine[J].Knowledge-Based Systems,2017,116(1):58-73. [47] ZHU F,YE N,YU W,et al.Boundary detection and sample reduction for one-class Support Vector Machines[J].Neurocomputing,2014,123:166-173. [48] LI C L,LIU Z D,HUI K H.Boundary Sample Selection Method Based on Cosine Similarity [J].Computer and Modernization,2017(8):66-70. [49] ZHANG A A,ZHENG P,FANG L,et al.A Sample Reduction Method for SVDD and Its Application[J].Jiangxi Science,2014,32(6):884-889. [50] PAN D,YIN Y,SUN Y,et al.Sample Selection in Support Vector Machines:A Fixed Neighborhood Sphere Approach[C]//2016 3rd International Conference on Information Science and Control Engineering (ICISCE).IEEE,2016. [51] LIU C,WANG W,WANG M,et al.An efficient instance selection algorithm to reconstruct training set for support vector machine[J].Knowledge-Based Systems,2017,116(1):58-73. [52] KANGAS J.Prototype Search for a Nearest Neighbor Classifier by a Genetic Algorithm[C]//International Conference on Computational Intelligence & Multimedia Applications.IEEE,1999. [53] AMIREZ-CRUZ J F,FUENTES O,ALARCON-AQUINO V,et al.Instance Selection and Feature Weighting Using Evolutionary Algo-rithms[C]//2006 15th International Conference on Computing.IEEE,2006. [54] NALEPA J,KAWULOK M.Adaptive Genetic Algorithm to Select Training Data for Support Vector Machines[M]//Applications of Evolutionary Computation.Springer Berlin Heidelberg,2014. [55] KAWULOK M,NALEPA J.Dynamically Adaptive Genetic Algorithm to Select Training Data for SVMs[M]//Advances in Artificial Intelligence-IBERAMIA 2014.2014. [56] KAWULOK M,NALEPA J,DUDZIK W.An Alternating Genetic Algorithm for Selecting SVM Model and Training Set[C]//Mexican Conference on Pattern Recognition.Cham:Springer,2017. [57] OTHMAN O M.Instance-Reduction Method based on Ant Colony Optimization[C]//Proceedings of the 2018 10th International Con-ference on Machine Learning and Computing.ACM,2018:47-53. [58] WANG J,NESKOVIC P,COOPERL N.Selecting Data for Fast Support Vector Machines Training[M]//Trends in Neural Computation.2007. [59] HARA K,NAKAYAMA K,KARAF A A M.A Training Data Selection In On-Line Training For Multilayer Neural Networks[C]//IEEE World Congress on IEEE International Joint Conference on Neural Networks.IEEE,2017. [60] WANG Z Y,WANG M W,ZUO J L,et al.The New Boundary Sample Selection Method and Its Application in the Text Classification [J].Journal of Jiangxi Normal University(Natural Science Edition),2019,43(1):76-83. |
[1] | LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214. |
[2] | NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296. |
[3] | HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11. |
[4] | LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266. |
[5] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[6] | ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343. |
[7] | ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119. |
[8] | CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323. |
[9] | XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406. |
[10] | WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423. |
[11] | SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440. |
[12] | YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515. |
[13] | LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92. |
[14] | ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99. |
[15] | WANG Fei, HUANG Tao, YANG Ye. Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion [J]. Computer Science, 2022, 49(6A): 784-789. |
|