Editorial

Chawla, Nitesh V.; Japkowicz, Nathalie; Kotcz, Aleksander

doi:10.1145/1007730.1007733

Cited by 1,739 publications

(238 citation statements)

References 27 publications

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“…In this paper, we consider only two-class case [5], [17]. The title of a smaller class is a minority class, and that of a bigger class is a majority class.…”

Section: Introductionmentioning

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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“…In this paper, we consider only two-class case [5], [17]. The title of a smaller class is a minority class, and that of a bigger class is a majority class.…”

Section: Introductionmentioning

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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show abstract

“…Generally, as oversampling gave better results than under-sampling, it turns out that under-sampling is not always as effective as has been claimed [6].…”

Section: In the Mg Sentence Np [το θέμα ηης ζυζήηηζης] Vp[είναι] Np[ηmentioning

confidence: 90%

“…Resamp le produces a random subsample of a dataset, namely samp ling with replacement. SM OTE is an over-samp ling method [6] that generates synthetic examples in a less application-specific manner, by operating in "feature space rather than data space". Our final results show that feature selection is a very competitive method, as proven by different approaches [7].…”

Section: Data Collectionmentioning

confidence: 99%

Learning Shallow Syntactic Dependencies from Imbalanced Datasets: A Case Study in Modern Greek and English

Karozou

Kermanidis

2011

IFIP Advances in Information and Communication Technology

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“…Much work has been done in considering non-uniform misclassification costs (alone), such as [4,5,7]. Those works can often used to solve problem of learning with very imbalanced datasets [3]. Some previous work, such as [10,12], consider the test cost alone without incorporating misclassification cost.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Hybrid Cost-Sensitive Decision Tree

Sheng

Ling

2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. Cost-sensitive decision tree and cost-sensitive naïve Bayes are both new cost-sensitive learning models proposed recently to minimize the total cost of test and misclassifications. Each of them has its advantages and disadvantages. In this paper, we propose a novel cost-sensitive learning model, a hybrid cost-sensitive decision tree, called DTNB, to reduce the minimum total cost, which integrates the advantages of cost-sensitive decision tree and of the cost-sensitive naïve Bayes together. We empirically evaluate it over various test strategies, and our experiments show that our DTNB outperforms cost-sensitive decision and the cost-sensitive naïve Bayes significantly in minimizing the total cost of tests and misclassification based on the same sequential test strategies, and single batch strategies.

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Editorial

Cited by 1,739 publications

References 27 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

Learning Shallow Syntactic Dependencies from Imbalanced Datasets: A Case Study in Modern Greek and English

Hybrid Cost-Sensitive Decision Tree

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