SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

2011 ◽  
Vol 33 (2) ◽  
pp. 245-265 ◽  
Author(s):  
Enislay Ramentol ◽  
Yailé Caballero ◽  
Rafael Bello ◽  
Francisco Herrera
Author(s):  
Tran Thanh Huyen

The robustness of rough sets theory in data cleansing have been proved in many studies. Recently, fuzzy rough set also make a deal with imbalanced data by two approaches. The first is a combination of fuzzy rough instance selection and balancing methods. The second tries to use different criteria to clean majorities and minorities classes of imbalanced data. This work is an extension of the second method which was presented in [16]. The paper depicts complete study about the second method with some proposed algorithms. It focuses mainly on binary classification with kNN and SVM for imbalanced data. Experiments and comparisons among related methods will confirm pros and coin of each method with respect to performance accuracy and time consumption.


2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.


2011 ◽  
Vol 130-134 ◽  
pp. 1681-1685 ◽  
Author(s):  
Guang Tian ◽  
Hao Tian ◽  
Guang Sheng Liu ◽  
Jin Hui Zhao ◽  
Li Ping Luo

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field, then a new method of compound-fault diagnosis was presented. The vibration signals at start-up in the gearbox are non-stationary signals, and traditional ways of diagnosis have low precision. Order tracking and wavelet packet and rough sets theory are introduced in the compound-fault diagnosis of bearing. First, the vibration signals at start-up were resampled using computer order tracking arithmetic and equal angle distributed vibration signals were obtained, and wavelet packet has been used for equal angle distributed vibration signals decomposition and reconstruction. Then, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound-fault example proves that the proposed method has high validity and more amplitude appliance foreground.


Author(s):  
Hirofumi Toyama ◽  
Tomonobu Senjyu ◽  
Shantanu Chakraborty ◽  
Atsushi Yona ◽  
Toshihisa Funabashi ◽  
...  

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