A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers. This paper proposes a cost-sensitive ensemble method based on cost-sensitive support vector machine (SVM), and query-by-committee (QBC) to solve imbalanced data classification. The proposed method first divides the majority-class dataset into several subdatasets according to the proportion of imbalanced samples and trains subclassifiers using AdaBoost method. Then, the proposed method generates candidate training samples by QBC active learning method and uses cost-sensitive SVM to learn the training samples. By using 5 class-imbalanced datasets, experimental results show that the proposed method has higher area under ROC curve (AUC), F-measure, and G-mean than many existing class-imbalanced learning methods.

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Imbalanced data classification based on hybrid resampling and twin support vector machine

Computer Science and Information Systems ◽

10.2298/csis161221017l ◽

2017 ◽

Vol 14 (3) ◽

pp. 579-595 ◽

Cited By ~ 2

Author(s):

Lu Cao ◽

Hong Shen

Keyword(s):

Support Vector Machine ◽

Real Life ◽

Imbalanced Data ◽

Data Classification ◽

Training Data ◽

Twin Support Vector Machine ◽

Support Vector ◽

Imbalanced Datasets ◽

Minority Class ◽

Imbalanced Data Classification

Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. As a variant of enhanced support vector machine (SVM), the twin support vector machine (TWSVM) provides an effective technique for data classification. TWSVM is based on a relative balance in the training sample dataset and distribution to improve the classification accuracy of the whole dataset, however, it is not effective in dealing with imbalanced data classification problems. In this paper, we propose to combine a re-sampling technique, which utilizes oversampling and under-sampling to balance the training data, with TWSVM to deal with imbalanced data classification. Experimental results show that our proposed approach outperforms other state-of-art methods.

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

Pakistan Journal of Statistics and Operation Research ◽

10.18187/pjsor.v17i2.3640 ◽

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.1756 ◽

2014 ◽

Vol 989-994 ◽

pp. 1756-1761 ◽

Cited By ~ 3

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.

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