An Empirical Study on the Performance of Cost-Sensitive Boosting Algorithms with Different Levels of Class Imbalance

Cost-sensitive boosting algorithms have proven successful for solving the difficult class imbalance problems. However, the influence of misclassification costs and imbalance level on the algorithm performance is still not clear. The present paper aims to conduct an empirical comparison of six representative cost-sensitive boosting algorithms, including AdaCost, CSB1, CSB2, AdaC1, AdaC2, and AdaC3. These algorithms are thoroughly evaluated by a comprehensive suite of experiments, in which nearly fifty thousands classification models are trained on 17 real-world imbalanced data sets. Experimental results show that AdaC serial algorithms generally outperform AdaCost and CSB when dealing with different imbalance level data sets. Furthermore, the optimality of AdaC2 algorithm stands out around the misclassification costs setting:CN=0.7,CP=1, especially for dealing with strongly imbalanced data sets. In the case of data sets with a low-level imbalance, there is no significant difference between the AdaC serial algorithms. In addition, the results indicate that AdaC1 is comparatively insensitive to the misclassification costs, which is consistent with the finding of the preceding research work.

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Classifying Imbalanced Data Sets by a Novel RE-Sample and Cost-Sensitive Stacked Generalization Method

Mathematical Problems in Engineering ◽

10.1155/2018/5036710 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

Jianhong Yan ◽

Suqing Han

Keyword(s):

Learning Community ◽

Class Imbalance ◽

Imbalanced Data ◽

Data Sets ◽

Class Imbalance Problem ◽

Imbalanced Data Sets ◽

Imbalance Data ◽

Imbalance Problem ◽

Stacked Generalization ◽

Model Generalization

Learning with imbalanced data sets is considered as one of the key topics in machine learning community. Stacking ensemble is an efficient algorithm for normal balance data sets. However, stacking ensemble was seldom applied in imbalance data. In this paper, we proposed a novel RE-sample and Cost-Sensitive Stacked Generalization (RECSG) method based on 2-layer learning models. The first step is Level 0 model generalization including data preprocessing and base model training. The second step is Level 1 model generalization involving cost-sensitive classifier and logistic regression algorithm. In the learning phase, preprocessing techniques can be embedded in imbalance data learning methods. In the cost-sensitive algorithm, cost matrix is combined with both data characters and algorithms. In the RECSG method, ensemble algorithm is combined with imbalance data techniques. According to the experiment results obtained with 17 public imbalanced data sets, as indicated by various evaluation metrics (AUC, GeoMean, and AGeoMean), the proposed method showed the better classification performances than other ensemble and single algorithms. The proposed method is especially more efficient when the performance of base classifier is low. All these demonstrated that the proposed method could be applied in the class imbalance problem.

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A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

Mathematical Problems in Engineering ◽

10.1155/2014/358942 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Qing-Yan Yin ◽

Jiang-She Zhang ◽

Chun-Xia Zhang ◽

Nan-Nan Ji

Keyword(s):

Evaluation Criteria ◽

Class Imbalance ◽

Imbalanced Data ◽

Data Classification ◽

Data Sets ◽

Imbalanced Data Sets ◽

Ensemble Pruning ◽

Imbalanced Data Classification ◽

Selective Ensemble ◽

Nonparametric Statistical

Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling,RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show thatRotEasypossesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.

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Difficulty Factors and Preprocessing in Imbalanced Data Sets: An Experimental Study on Artificial Data

Foundations of Computing and Decision Sciences ◽

10.1515/fcds-2017-0007 ◽

2017 ◽

Vol 42 (2) ◽

pp. 149-176 ◽

Cited By ~ 7

Author(s):

Szymon Wojciechowski ◽

Szymon Wilk

Keyword(s):

Experimental Study ◽

Class Imbalance ◽

Imbalanced Data ◽

Classification Performance ◽

Data Sets ◽

Artificial Data ◽

Minority Class ◽

Imbalanced Data Sets ◽

The Impact

Abstract In this paper we describe results of an experimental study where we checked the impact of various difficulty factors in imbalanced data sets on the performance of selected classifiers applied alone or combined with several preprocessing methods. In the study we used artificial data sets in order to systematically check factors such as dimensionality, class imbalance ratio or distribution of specific types of examples (safe, borderline, rare and outliers) in the minority class. The results revealed that the latter factor was the most critical one and it exacerbated other factors (in particular class imbalance). The best classification performance was demonstrated by non-symbolic classifiers, particular by k-NN classifiers (with 1 or 3 neighbors - 1NN and 3NN, respectively) and by SVM. Moreover, they benefited from different preprocessing methods - SVM and 1NN worked best with undersampling, while oversampling was more beneficial for 3NN.

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Imbalanced Data Detection Kernel Method in Closed Systems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3652 ◽

2013 ◽

Vol 756-759 ◽

pp. 3652-3658

Author(s):

You Li Lu ◽

Jun Luo

Keyword(s):

Kernel Methods ◽

Kernel Method ◽

Imbalanced Data ◽

Data Detection ◽

Data Sets ◽

System Call ◽

Data Set ◽

Imbalanced Data Sets ◽

Lower Complexity ◽

Closed Systems

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.

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