Computer Science ›› 2017, Vol. 44 ›› Issue (8): 225-229.doi: 10.11896/j.issn.1002-137X.2017.08.038
Previous Articles Next Articles
GAO Feng and HUANG Hai-yan
| [1] KRAWCZYK B,WOZ＇niak M.Hypertension Type Classification Using Hierarchical Ensemble of One-Class Classifiers for Imba-lanced Data[M]∥ICT Innovations 2014.Springer International Publishing,2015:341-349. [2] CAO P,LI B,LI W,et al.Hybrid Sampling Algorithm Based on Probability Distribution Estimation[J].Control and Decision,2014(5):815-520.(in Chinese) 曹鹏,李博,栗伟,等.基于概率分布估计的混合采样算法[J].控制与决策,2014(5):815-520. [3] CHAO W L,LIU J Z,DING J J.Facial age estimation based on label-sensitive learning and age-oriented regression[J].Pattern Recognition,2013,46(3):628-641. [4] ZHANG D,ISLAM M M,LU G.A review on automatic image annotation techniques[J].Pattern Recognition,2012,45(1):346-362. [5] LI J,LI H,YU J L.Application of Random-SMOTE on Imba-lanced Data Mining[C]∥2011 Fourth International Conference on Business Intelligence and Financial Engineering(BIFE).2011:130-133. [6] RAMENTOL E,CABALLERO Y,BELLO R,et al.SMOTE-RSB*:a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory[J].Knowledge & Information Systems,2011,33(2):245-265. [7] CHAWLA N V,CIESLAK D A,HALL L O,et al.Automatically countering imbalance and its empirical relationship to cost[J].Data Mining and Knowledge Discovery,2008,17(2):225-252. [8] WANG S,YAO X.Diversity analysis on imbalanced data sets by using ensemble models[C]∥IEEE Symposium on Computatio-nal Intelligence and Data Mining,2009(CIDM’09).IEEE,2009:535-548. [9] BASZCZY S J,STEFANOWSKI J.Neighbourhood samplingin bagging for imbalanced data[J].Neurocomputing,2015,150:529-542. [10] CHAWLA N V,LAZAREVIC A,HALL L O,et al.SMOTE-Boost:Improving Prediction of the Minority class in Boosting[J].Lecture Notes in Computer Science,2003,8:107-119. [11] LI X F,LI J,DONG Y F,et al.A New Learning Algorithm for Imbalanced Data-PCBoost[J].Chinese Journal of Computers,2012,5(2):202-209.(in Chinese).李雄飞,李军,董元方,等.一种新的不平衡数据学习算法PCBoost[J].计算机学报,2012,35(2):202-209. [12] LI K W,YANG L,LIU W Y,et al.Classification Method of Imbalanced Data Based on RSBoost[J].Computer Science,2015,2(9):249-252.(in Chinese) 李克文,杨磊,刘文英,等.基于RSBoost算法的不平衡数据分类方法[J].计算机科学,2015,42(9):249-252. [13] NAPIERA,KRYSTYNA A,STEFANOWSKI J,et al.Lear- ning from imbalanced data in presence of noisy and borderline examples[C]∥International Conference on Rough Sets and Current Trends in Computing.Springer-Verlag,2010:158-167. [14] NAPIERALA K,STEFANOWSKI J.Identification of different types of minority class examples in imbalanced data[C]∥International Conference on Hybrid Artificial Intelligent Systems.Springer-Verlag,2012:139-150. [15] WEISS G M.The impact of small disjuncts on classifier learning[M]∥Data Mining.Springer US,2010:193-226. [16] NAPIERALA K.Improving rule classifiers for imbalanced data[D].Poznan University of Technology,2013. [17] WILSON D R,MARTINEZ T R.Improved heterogeneous distance functions[J].Journal of Artificial Intelligence Research,2000,6(1):1-34. [18] LI L,ZOU B,HU Q,et al.Dynamic classifier ensemble using classification confidence[J].Neurocomputing,2013,99(99):581-591. [19] JAPKOWICZ N,SHAH M.Evaluating Learning Algorithms:A Classification Perspective.http://www.openisbn.com/download/0521196000.pdf. |
| No related articles found! |
|
||
Home