An Efficient Over-sampling Approach Based on Mean Square Error Back-propagation for Dealing with the Multi-class Imbalance Problem

[1]  Roberto Alejo,et al.  A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios , 2013, Pattern Recognit. Lett..

[2]  Gary William Grewal,et al.  A Dynamic Sampling Framework for Multi-class Imbalanced Data , 2012, 2012 11th International Conference on Machine Learning and Applications.

[3]  Xin Yao,et al.  Multiclass Imbalance Problems: Analysis and Potential Solutions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  César Hervás-Martínez,et al.  Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity , 2011, Expert Syst. Appl..

[5]  Pedro Antonio Gutiérrez,et al.  A dynamic over-sampling procedure based on sensitivity for multi-class problems , 2011, Pattern Recognit..

[6]  Sang-Hoon Oh,et al.  Error back-propagation algorithm for classification of imbalanced data , 2011, Neurocomputing.

[7]  David E. Goldberg,et al.  Facetwise Analysis of XCS for Problems With Class Imbalances , 2009, IEEE Transactions on Evolutionary Computation.

[8]  S. García,et al.  Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy , 2009, Evolutionary Computation.

[9]  Foster Provost,et al.  Machine Learning from Imbalanced Data Sets 101 , 2008 .

[10]  David A. Cieslak,et al.  Automatically countering imbalance and its empirical relationship to cost , 2008, Data Mining and Knowledge Discovery.

[11]  José Salvador Sánchez,et al.  On the use of surrounding neighbors for synthetic over-sampling of the minority class , 2008 .

[12]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[13]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[14]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[15]  Sven F. Crone,et al.  The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing , 2006, Eur. J. Oper. Res..

[16]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[17]  Jing Peng,et al.  Classifying Unbalanced Pattern Groups by Training Neural Network , 2006, ISNN.

[18]  Nicolaos B. Karayiannis,et al.  Feedforward neural network models for handling class overlap and class imbalance , 2005, Int. J. Neural Syst..

[19]  Gustavo E. A. P. A. Batista,et al.  Balancing Strategies and Class Overlapping , 2005, IDA.

[20]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[21]  Hong Guo,et al.  Neural Learning from Unbalanced Data , 2004, Applied Intelligence.

[22]  Yi Lu Murphey,et al.  Multiclass pattern classification using neural networks , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[23]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[24]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[25]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[26]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

[27]  Okyay Kaynak,et al.  An algorithm for fast convergence in training neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[28]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[29]  T. G. Clarkson,et al.  Adaptive algorithm for training pRAM neural networks on unbalanced data sets , 1998 .

[30]  Lorenzo Bruzzone,et al.  Classification of imbalanced remote-sensing data by neural networks , 1997, Pattern Recognit. Lett..

[31]  Kishan G. Mehrotra,et al.  An improved algorithm for neural network classification of imbalanced training sets , 1993, IEEE Trans. Neural Networks.

[32]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[33]  Yi Lu Murphey,et al.  Multi-class pattern classification using neural networks , 2007, Pattern Recognit..

[34]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[35]  U Aickelin,et al.  Handbook of metaheuristics (International series in operations research and management science) , 2005 .

[36]  Pablo Moscato,et al.  A Gentle Introduction to Memetic Algorithms , 2003, Handbook of Metaheuristics.

[37]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[38]  R. Iman,et al.  Approximations of the critical region of the fbietkan statistic , 1980 .