Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

Han, Hui; Wang, Wenyuan; Mao, Binghuan

doi:10.1007/11538059_91

Cited by 2,818 publications

(1,635 citation statements)

References 15 publications

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“…(2005) designed the improvement of SMOTE, namely Borderline-SMOTE [10]. The authors divided positive instances into three regions; noise, borderline, and safe, by considering the number of negative instances on k nearest neighbours.…”

Section: Related Workmentioning

confidence: 99%

Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

Bunkhumpornpat

Sinapiromsaran

Lursinsap

2009

Lecture Notes in Computer Science

724

317

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Abstract. The class imbalanced problem occurs in various disciplines when one of target classes has a tiny number of instances comparing to other classes. A typical classifier normally ignores or neglects to detect a minority class due to the small number of class instances. SMOTE is one of over-sampling techniques that remedies this situation. It generates minority instances within the overlapping regions. However, SMOTE randomly synthesizes the minority instances along a line joining a minority instance and its selected nearest neighbours, ignoring nearby majority instances. Our technique called SafeLevel-SMOTE carefully samples minority instances along the same line with different weight degree, called safe level. The safe level computes by using nearest neighbour minority instances. By synthesizing the minority instances more around larger safe level, we achieve a better accuracy performance than SMOTE and Borderline-SMOTE.

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Section: Related Workmentioning

confidence: 99%

Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

Bunkhumpornpat

Sinapiromsaran

Lursinsap

2009

Lecture Notes in Computer Science

724

317

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“…At the data level, various re-sampling techniques are applied to balance class distribution, including over-sampling minority class instances and under-sampling majority class instances [5], [6], [7], [8] . Particularly, SMOTE (Synthetic Minority Over-sampling Technique) [1] is a popular approach designed for generating new minority class data, which could expand decision boundary towards majority class.…”

Section: Introduction I Mbalanced Data Sets (Ids) Correspond To Domentioning

confidence: 99%

Diversity analysis on imbalanced data sets by using ensemble models

Wang

Yao

2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

425

240

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Abstract-Many real-world applications have problems when learning from imbalanced data sets, such as medical diagnosis, fraud detection, and text classification. Very few minority class instances cannot provide sufficient information and result in performance degrading greatly. As a good way to improve the classification performance of weak learner, some ensemblebased algorithms have been proposed to solve class imbalance problem. However, it is still not clear that how diversity affects classification performance especially on minority classes, since diversity is one influential factor of ensemble. This paper explores the impact of diversity on each class and overall performance. As the other influential factor, accuracy is also discussed because of the trade-off between diversity and accuracy. Firstly, three popular re-sampling methods are combined into our ensemble model and evaluated for diversity analysis, which includes under-sampling, over-sampling, and SMOTE [1] -a data generation algorithm. Secondly, we experiment not only on two-class tasks, but also those with multiple classes. Thirdly, we improve SMOTE in a novel way for solving multi-class data sets in ensemble model -SMOTEBagging.

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“…From these three general topics in class imbalance, data level methods are the most investigated. These methods consist of balancing the original data set, either by over-sampling the minority class [8][9][10][11] and/or by under-sampling the majority class [12][13][14], until the problem classes are approximately equally represented.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Performance of Resampling Strategies for the Class Imbalance Problem

García¹,

Sánchez²,

Mollineda³

2010

Trends in Applied Intelligent Systems

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Abstract. The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with the ratio between minority and majority classes. Experiments using eight different classifiers show that the most significant differences are for data sets with low or moderate imbalance: over-sampling clearly appears as better than under-sampling for local classifiers, whereas some under-sampling strategies outperform over-sampling when employing classifiers with global learning.

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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

Cited by 2,818 publications

References 15 publications

Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

Diversity analysis on imbalanced data sets by using ensemble models

Exploring the Performance of Resampling Strategies for the Class Imbalance Problem

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