Learning from imbalanced data sets with a Min-Max modular support vector machine

2011 ◽  
Vol 6 (1) ◽  
pp. 56-71 ◽  
Author(s):  
Lu Bao-Liang ◽  
Wang Xiao-Lin ◽  
Yang Yang ◽  
Zhao Hai
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Imbalanced Data Sets

Affinity and class probability-based fuzzy support vector machine for imbalanced data sets

2020 ◽  
Vol 122 ◽  
pp. 289-307 ◽  
Author(s):  
Xinmin Tao ◽  
Qing Li ◽  
Chao Ren ◽  
Wenjie Guo ◽  
Qing He ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Fuzzy Support Vector Machine ◽  
Imbalanced Data Sets ◽  
Class Probability

Imbalanced Data Classification Using Cost-Sensitive Support Vector Machine Based on Information Entropy

2014 ◽  
Vol 989-994 ◽  
pp. 1756-1761 ◽  
Author(s):  
Wei Duan ◽  
Liang Jing ◽  
Xiang Yang Lu
Keyword(s):  
Support Vector Machine ◽  
Information Entropy ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Classification Problems ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Penalty Factor ◽  
Imbalanced Data Classification

As a supervised classification algorithm, Support Vector Machine (SVM) has an excellent ability in solving small samples, nonlinear and high dimensional classification problems. However, SVM is inefficient for imbalanced data sets classification. Therefore, a cost sensitive SVM (CSSVM) should be designed for imbalanced data sets classification. This paper proposes a method which constructed CSSVM based on information entropy, and in this method the information entropies of different classes of data set are used to determine the values of penalty factor of CSSVM.

Classification with Local Clustering in Imbalanced Data Sets

2011 ◽  
Vol 219-220 ◽  
pp. 151-155 ◽  
Author(s):  
Hua Ji ◽  
Hua Xiang Zhang
Keyword(s):  
Data Distribution ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Local Clustering ◽  
Rare Class ◽  
Novel Method ◽  
The Cost

In many real-world domains, learning from imbalanced data sets is always confronted. Since the skewed class distribution brings the challenge for traditional classifiers because of much lower classification accuracy on rare classes, we propose the novel method on classification with local clustering based on the data distribution of the imbalanced data sets to solve this problem. At first, we divide the whole data set into several data groups based on the data distribution. Then we perform local clustering within each group both on the normal class and the disjointed rare class. For rare class, the subsequent over-sampling is employed according to the different rates. At last, we apply support vector machines (SVMS) for classification, by means of the traditional tactic of the cost matrix to enhance the classification accuracies. The experimental results on several UCI data sets show that this method can produces much higher prediction accuracies on the rare class than state-of-art methods.

scholarly journals A Novel Weighted Ensemble Method to Overcome the Impact of Under-fitting and Over-fitting on the Classification Accuracy of the Imbalanced Data Sets

2021 ◽  
pp. 483-496
Author(s):  
Ghulam Fatima ◽  
Sana Saeed
Keyword(s):  
Data Mining ◽  
Imbalanced Data ◽  
Ensemble Method ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Imbalanced Data Sets ◽  
Sampling Procedures ◽  
The Impact

In the data mining communal, imbalanced class dispersal data sets have established mounting consideration. The evolving field of data mining and information discovery seeks to establish precise and effective computational tools for the investigation of such data sets to excerpt innovative facts from statistics. Sampling methods re-balance the imbalanced data sets consequently improve the enactment of classifiers. For the classification of the imbalanced data sets, over-fitting and under-fitting are the two striking problems. In this study, a novel weighted ensemble method is anticipated to diminish the influence of over-fitting and under-fitting while classifying these kinds of data sets. Forty imbalanced data sets with varying imbalance ratios are engaged to conduct a comparative study. The enactment of the projected method is compared with four customary classifiers including decision tree(DT), k-nearest neighbor (KNN), support vector machines (SVM), and neural network (NN). This evaluation is completed with two over-sampling procedures, an adaptive synthetic sampling approach (ADASYN), and a synthetic minority over-sampling (SMOTE) technique. The projected scheme remained efficacious in diminishing the impact of over-fitting and under-fitting on the classification of these data sets.

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