Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 488-493.doi: 10.11896/JsJkx.190600132
• Database & Big Data & Data Science • Previous Articles Next Articles
CUI Wei, JIA Xiao-lin, FAN Shuai-shuai and ZHU Xiao-yan
CLC Number:
| [1] HIERONS R.Machine learning.Tom M.Published by McGraw-Hill,Maidenhead,U.K.,International Student Edition,1997.ISBN:0-07-115467-1,414 pages.Price:U.K.￡22.99,soft co-ver.Software Testing Verification & Reliability,2015,9(3):191-193. [2] SALZBERG S L J M L.C4.5:Programs for Machine Learning by J.Ross Quinlan.Morgan Kaufmann Publishers,Inc.,1993.Machine Learning,1994,16(3):235-240. [3] RAJPUT A.J48 and JRIP Rules for E-Governance Data.IJCSS,2011,5(2):201. [4] FRNKRANZ J,WIDMER G.Incremental Reduced ErrorPruning//Machine Learning Proceedings.1994:70-77. [5] HU K,LU Y,ZHOU L,et al.Integrating classification and association rule mining:A concept lattice framework//International Workshop on Rough Sets,Fuzzy Sets,Data Mining,and Granular-Soft Computing.Springer.1999:443-447. [6] LI W,HAN J,PEI J.CMAR:Accurate and efficient classification based on multiple class-association rules//Proceedings IEEE International Conference on Data Mining,2001(ICDM 2001).IEEE,2001:369-376. [7] THABTAH F A,COWLING P,PENG Y.MMAC:A New Multi-Class,Multi-Label Associative Classification Approach//IEEE International Conference on Data Mining.2004. [8] ZHU X,SONG Q,JIA Z.A Weighted Voting-Based Associative Classification Algorithm.The Computer Journal,2010,53(6):786-801. [9] GANGANWAR V.An overview of classification algorithms for imbalanced datasets.International Journal of Emerging Technology and Advanced Engineering,2012,2(4):42-47. [10] HE H,MA Y.Imbalanced learning.Foundations,algorithms, and applications.Wiley-IEEE Press,2013. [11] ZHOU Z H,LIU X Y.On multi-class cost-sensitive learning//National Conference on Artificial Intelligence.2006. [12] WU G,CHANG E Y.KBA:Kernel boundary alignment consi-dering imbalanced data distribution.IEEE Transactions on Knowledge & Data Engineering,2005(6):786-795. [13] BREIMAN L.Bagging predictors.Machine Learning,1996, 24(2):123-140. [14] ZAREAPOOR M,SHAMSOLMOALI P.Application of credit card fraud detection:Based on bagging ensemble classifier.Procedia computer science,2015,48(2015):679-685. [15] WITTEN I H,FRANKE,HALL M A,et al.Data Mining: Practical machine learning tools and techniques.Morgan Kaufmann,2016:70-71. [16] 韩家炜,坎伯.数据挖掘:概念与技术.北京:机械工业出版社,2012:158-159. [17] DEORA C S,ARORA S,MAKANI Z.Comparison ofInteres-tingness Measures:Support-Confidence Framework versus Lift-Irule Framework.International Journal of Enginnering Research & Applications,2014,3(2):208-215. [18] ALCAL-FDEZ J,FERNNDEZ A,LUENGO J,et al.KEEL Data-Mining Software Tool:Data Set Repository,Integration of Algorithms and Experimental Analysis Framework.Journal of Multiple-Valued Logic & Soft Computing,2011,17:255-287. [19] PATIL T R,SHEREKAR S.Performance analysis of Naive Bayes and J48 classification algorithm for data classification.International Journal of Computer Science and Applications,2013,6(2):256-261. [20] QUINLAN J R.Bagging,boosting,and C4.5//AAAI/IAAI.1996:725-730. [21] LOBO J M,JIMNEZ-VALVERDE A,REAL R.AUC:a misleading measure of the performance of predictive distribution models.Global Ecology and Biogeography,2008,17(2):145-151. [22] DAVIS J,GOADRICH M.The relationship between Precision-Recall and ROC curves//Proceedings of the 23rd International Conference on Machine Learning.ACM,2006:233-240. [23] POWERS D M.Evaluation:from precision,recall and F-measure to ROC,informedness,markedness and correlation.Journal of Machine Learning Technology,2011,2(1):37-63. [24] WILCOXON F,KATTI S,WILCOX R A.Critical values and probability levels for the Wil-coxon rank sum test and the Wil-coxon signed rank test.Selected Tables in Mathematical Statistics,1970,1:171-259. |
| [1] | CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32. |
| [2] | ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138. |
| [3] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
| [4] | WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25. |
| [5] | TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216. |
| [6] | YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236. |
| [7] | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49. |
| [8] | YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63. |
| [9] | ZHANG Hong-bo, DONG Li-jia, PAN Yu-biao, HSIAO Tsung-chih, ZHANG Hui-zhen, DU Ji-xiang. Survey on Action Quality Assessment Methods in Video Understanding [J]. Computer Science, 2022, 49(7): 79-88. |
| [10] | DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65. |
| [11] | LI Xiao-wei, SHU Hui, GUANG Yan, ZHAI Yi, YANG Zi-ji. Survey of the Application of Natural Language Processing for Resume Analysis [J]. Computer Science, 2022, 49(6A): 66-73. |
| [12] | DENG Kai, YANG Pin, LI Yi-zhou, YANG Xing, ZENG Fan-rui, ZHANG Zhen-yu. Fast and Transmissible Domain Knowledge Graph Construction Method [J]. Computer Science, 2022, 49(6A): 100-108. |
| [13] | HUANG Shao-bin, SUN Xue-wei, LI Rong-sheng. Relation Classification Method Based on Cross-sentence Contextual Information for Neural Network [J]. Computer Science, 2022, 49(6A): 119-124. |
| [14] | LIN Xi, CHEN Zi-zhuo, WANG Zhong-qing. Aspect-level Sentiment Classification Based on Imbalanced Data and Ensemble Learning [J]. Computer Science, 2022, 49(6A): 144-149. |
| [15] | KANG Yan, WU Zhi-wei, KOU Yong-qi, ZHANG Lan, XIE Si-yu, LI Hao. Deep Integrated Learning Software Requirement Classification Fusing Bert and Graph Convolution [J]. Computer Science, 2022, 49(6A): 150-158. |
|
||
Home