[1] ESKIN E, ARNOLD A, PRERAU M, et al. A geometric framework for unsupervised anomaly detection[J]. Applications of Data Mining in Computer Security, 2002, 6:77-101. [2] ISERMANN R, BALLE P. Trends in the application of model-based fault detection and diagnosis of technical processes[J]. Control Engineering Practice, 1997, 5(5):709-719. [3] KOU Y, LU C T, SIRWONGWATTANA S, et al. Survey of fraud detection techniques[C]//Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control. Piscataway, NJ:IEEE, 2004:749-754. [4] 王莉莉,付忠良,陶攀,等.基于主动学习不平衡多分类AdaBoost算法的心脏病分类[J].计算机应用,2017,37(7):1994-1998.(WANG L L, FU Z L, TAO P, et al. Heart disease classification based on active imbalance multi-class AdaBoost algorithm[J]. Journal of Computer Applications, 2017, 37(7):1994-1998.) [5] FU K, CHENG D, TU Y, et al. Credit card fraud detection using convolutional neural networks[C]//Proceedings of the 2016 International Conference on Neural Information Processing. Berlin:Springer, 2016:483-490. [6] DANENAS P, GARSVA G. Selection of support vector machines based classifiers for credit risk domain[J]. Expert Systems with Applications, 2015, 42(6):3194-3204. [7] MARTIN-DIAZ I, MORINIGO-SOTELO D, DUQUE-PEREZ O, et al. Early fault detection in induction motors using AdaBoost with imbalanced small data and optimized sampling[J]. IEEE Transactions on Industry Applications, 2017, 53(3):3066-3075. [8] 赵楠,张小芳,张利军.不平衡数据分类研究综述[J]. 计算机科学,2018,45(S1):22-27.(ZHAO N, ZHANG X F, ZHANG L J. Overview of imbalanced data classification[J]. Computer Science, 2018, 45(S1):22-27.) [9] IRTAZA A, ADNAN S M, AHMED K T, et al. An ensemble based evolutionary approach to the class imbalance problem with applications in CBIR[J]. Applied Sciences, 2018, 8(4):495. [10] GALAR M, FERNANDEZ A, BARRENECHEA E, et al. EUSBoost:enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling[J]. Pattern Recognition, 2013, 46(12):3460-3471. [11] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE:synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2011, 16(1):321-357. [12] LAURIKKALA J. Improving identification of difficult small classes by balancing class distribution[C]//Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe. Berlin:Springer, 2001:63-66. [13] KUBAT M, MATWIN S. Addressing the curse of imbalanced training sets:one-sided selection[C]//Proceedings of the 14th International Conference on Machine Learning. New York:ACM, 1997:179-186. [14] TAHIR M A, KITTLER J, MIKOLAJCZYK K, et al. A multiple expert approach to the class imbalance problem using inverse random under sampling[C]//Proceedings of the 8th International Workshop on Multiple Classifier Systems. Berlin:Springer, 2009:82-91. [15] IMRAN M, RAO V S, AMARASIMHA T, et al. A novel technique on class imbalance big data using analogous over sampling approach[J]. International Journal of Computational Intelligence Research, 2017, 13(10):2407-2417. [16] LIMA C F L, de ASSIS F M, de SOUZA C P. Decision tree based on Shannon, Rényi and Tsallis entropies for intrusion tolerant systems[C]//Proceedings of the 5th International Conference on Internet Monitoring and Protection. Piscataway, NJ:IEEE, 2010:117-122. [17] BOONCHUAY K, SINAPIROMSARAN K, LURSINSAP C. Decision tree induction based on minority entropy for the class imbalance problem[J]. Pattern Analysis and Applications, 2017, 20(3):769-782. [18] KIRSHNERS A, PARSHUTIN S, GORSKIS H. Entropy-based classifier enhancement to handle imbalanced class problem[J]. Procedia Computer Science, 2017, 104:586-591. [19] LI X, ZHAO H, ZHU W. A cost sensitive decision tree algorithm with two adaptive mechanisms[J]. Knowledge-Based Systems, 2015, 88:24-33. [20] 郑燕,王杨,郝青峰,等.用于不平衡数据分类的代价敏感超网络算法[J].计算机应用,2014,34(5):1336-1340.(ZHENG Y, WANG Y, HAO Q F, et al. Cost-sensitive hypernetworks for imbalanced data classification[J]. Journal of Computer Applications, 2014, 34(5):1336-1340.) [21] LEE S J, XU Z, LI T, et al. A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making[J]. Journal of Biomedical Informatics, 2018, 78:144-155. [22] QUINLAN J R. C4.5:Programs for Machine Learning[M]. San Francisco, CA:Morgan Kaufmann, 1993:17-26. [23] FRANK E, HALL M A, WITTEN L H. The WEKA workbench. online appendix for "Data mining:practical machine learning tools and techniques"[EB/OL]. (2016-11-22)[2018-05-04]. https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf. [24] DHANABAL L, SHANTHARAJAH S P. A study on NSL-KDD dataset for intrusion detection system based on classification algorithms[J]. International Journal of Advanced Research in Computer and Communication Engineering, 2015, 4(6):446-452. [25] ADEPU S, MATHUR A. An investigation into the response of a water treatment system to cyber attacks[C]//Proceedings of the 17th IEEE International Symposium on High Assurance Systems Engineering. Washington, DC:IEEE Computer Society, 2016:141-148. [26] GOH J, ADEPU S, JUNEJO K N, et al. A dataset to support research in the design of secure water treatment systems[C]//Proceedings of the 11th International Conference on Critical Information Infrastructures Security. Berlin:Springer, 2016:88-99. |